BMC Neurosci 2017, 18(Suppl 1):60
DOI 10.1186/s12868-017-0372-1
BMC Neuroscience
Open Access
MEETING ABSTRACTS
26th Annual Computational Neuroscience
Meeting (CNS*2017): Part 3
Antwerp, Belgium. 15–20 July 2017
Published: 17 August 2017
P156
Multiscale modeling of ischemic stroke with the NEURON
reaction‑diffusion module
Adam J. H. Newton1,2, Alexandra H. Seidenstein2,3, Robert A. McDougal1,
William W. Lytton2,4
1
Department of Neuroscience, Yale University, New Haven, CT 06520,
USA; 2Department Physiology & Pharmacology, SUNY Downstate,
Brooklyn, NY 11203, USA; 3NYU School of Engineering, 6 MetroTech
Center, Brooklyn, NY 11201, USA; 4Kings County Hospital Center, Brooklyn,
NY 11203, USA
Correspondence: Adam J. H. Newton (adam.newton@yale.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P156
Ischemic stroke is fundamentally a multiscale phenomenon [1]. Occlusion of blood vessels in the brain triggers a cascade of changes including: 1. synaptic glutamate release, related to excitotoxicity; 2. elevated
extracellular potassium, leading to spreading depression; 3. cell swelling, reducing the extracellular volume and diffusion; 4. production of
reactive oxygen species, which give rise to inflammation. These cascades occur over multiple time-scales, with the initial rapid changes
in cell metabolism and ionic concentrations trigging several damaging agents that may ultimately leads to cell death. Tissue affected
by ischemic stroke is divided into three regions; 1. a core where cells
suffer irreparable damage and death, 2. a penumbra where cells may
recover with reperfusion, 3. a further region of edema where spontaneous recovery is expected. Multiscale modeling and multiphysics
modeling is essential to capture this cascade. Such modeling requires
coupling complex intracellular molecular alterations with electrophysiology, and consideration of network properties in the context of bulk
tissue alterations mediated by extracellular diffusion.
Spreading depression is a wave of depolarization that propagates
through tissue and causes cells in the penumbra to expend energy
by repolarization, increasing their vulnerability to cell death. We modeled the spreading depression seen in ischemic stroke by coupling a
detailed biophysical model of cortical pyramidal neurons equipped
with Na+/K+-ATPase pumps with reaction-diffusion of ions in the
extracellular space (ECS). A macroscopic view of the ECS is characterised by its tortuosity (a reduction in the diffusion coefficient due to
obstructions) and its free volume fraction (typically ~20%). The addition of reactions allows the ECS be modeled as an active medium glial
buffering of K+. Ischemia impedes ATP production which results in a
failure of the Na+/K+-ATPase pump and a rise in extracellular K+. Once
extracellular K+ exceeds a threshold it will cause neurons to depolarize, further increasing extracellular K+.
NEURON’s reaction-diffusion module NRxD [2] provides a platform
where detailed neurons models can be embedded in a macroscopic
model of tissue. This is demonstrated with a multiscale biophysical
model of ischemic stroke where the rapid intracellular changes are
coupled with the slower diffusive signaling.
Acknowledgements
Research supported by NIH grant 5R01MH086638
References
1. Newton, AJH, and Lytton, WW: Computer modeling of ischemic stroke. Drug
Discovery Today: Disease Models. 2017.
2. McDougal RA, Hines ML, Lytton WW: Reaction-diffusion in the NEURON
simulator. Frontiers in neuroinformatics. 2013, 7(28).
P157
Accelerating NEURON reaction‑diffusion simulations
Robert A. McDougal1, William W. Lytton2,3
1
Neuroscience, Yale University, New Haven, CT 06520, USA; 2Physiology
& Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY 11203,
USA; 3Kings County Hospital, Brooklyn, NY 11203, USA
Correspondence: Robert A. McDougal (robert.mcdougal@yale.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P157
A neuron’s electrical activity is governed not just by presynaptic activity, but also by its internal state. This state is a function of history
including prior synaptic input (e.g. cytosolic calcium concentration,
protein expression in SCN neurons), cellular health, and routine biological processes. The NEURON simulator [1], like much of computational
neuroscience, has traditionally focused on electrophysiology. NEURON
has included NRxD to give standardized support for reaction-diffusion
(i.e. intracellular) modeling for the past 5 years [2], facilitating studies
into the role of electrical-chemical interactions. The original reactiondiffusion support was written in vectorized Python, which offered limited performance, but ongoing improvements have now significantly
reduced run-times, making larger-scale studies more practical.
New accelerated reaction-diffusion methods are being developed
as part of a separate NEURON module, crxd. This new module will
ultimately be a fully compatible replacement for the existing NRxD
module (rxd). Developing it as a separate module allows us to make it
available to the community before it supports the full functionality of
NRxD. The interface code for crxd remains in Python, but it now transfers model structure to C code via ctypes, which performs all run-time
calculations; Python is no longer invoked during simulation. Dynamic
code generation allows arbitrary reaction schemes to run at full compiled speed. Thread-based parallelization accelerates extracellular
reaction-diffusion simulations.
Preliminary tests suggest an approximately 10x reduction in 1D runtime using crxd instead of the Python-based rxd. Like rxd, crxd uses
the Hines method [3] for O(n) 1D reaction-diffusion simulations. Using
4 cores for extracellular diffusion currently reduces the runtime by a
factor of 2.3.
Additionally, using the crxd module simplifies
setup relative to rxd-based simulations since it does not require installing scipy.
Once crxd supports the entire documented NRxD interface and
has been thoroughly tested, it will replace the rxd module and thus
become NEURON’s default module for specifying reaction-diffusion
kinetics.
Acknowledgements
Research supported by NIH R01 MH086638.
© The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license,
and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/
publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
BMC Neurosci 2017, 18(Suppl 1):60
References
1. NEURON | for empirically based simulations of neurons and networks of
neurons [http://neuron.yale.edu]
2. McDougal RA, Hines ML, Lytton WW: Reaction-diffusion in the NEURON
simulator. Front. Neuroinform 2013, 7:28.
3. Hines M: Efficient computation of branched nerve equations. Int. J. BioMedical Computing 1984, 15:69–76.
P158
Computation of invariant objects in the analysis of periodically
forced neural oscillators
Alberto Pérez-Cervera, Gemma Huguet, Tere M-Seara
Departament de Matemàtica Aplicada, Universitat Politècnica de
Catalunya, Barcelona, E-08028, Spain
Correspondence: Alberto Pérez-Cervera (alberto.perez@upc.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P158
Background oscillations, reflecting the excitability of neurons, are
ubiquitous in the brain. Some studies have conjectured that when
spikes sent by one population reach the other population in the peaks
of excitability, then information transmission between two oscillating
neuronal groups is more effective [1]. In this context, the phase relationship between oscillating neuronal populations may have implications in neuronal communication between brain areas [2, 3]. The Phase
Response Curve (PRC) of a neural oscillator measures the phase-shift
resulting from perturbing the oscillator at different phases of the cycle.
It provides useful information to understand how phase-locking relationships between neural oscillators emerge but only when perturbations are weak and amplitude is not taken into account.
In this work, we consider a population rate model [4] and perturb it
with a time-dependent input. In order to study the phase-locking
relationships that emerge, we use the stroboscopic map to perform a
bifurcation analysis as a function of the amplitude and frequency of
the perturbation. We observe the existence of bistable solutions for
some regions of the parameters space, suggesting that, for a given
input, populations may operate in different regimes. Furthermore, we
apply powerful computational methods [5] to compute the invariant
objects for the stroboscopic map, providing a framework that enlarges
the PRC comprehension of the perturbative effects in the phase
dynamics.
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networks and their role in communication through coherence. Frontiers in
human neuroscience 2010, 4:196.
3. Canavier CC: Phase-resetting as a tool of information transmission. Current
opinion in neurobiology 2015, 31: 206–213.
4. Wilson HR, Cowan JD: Excitatory and inhibitory interactions in localized
populations of model neurons. Biophysical journal 1972, 12.1:1–24.
5. Haro À, Canadell M, Figueras JL, Luque A, Mondelo JM: The Parameterization Method for Invariant Manifolds 2016. Springer.
P159
Computational model of spatio‑temporal coding in CA3
with speed‑dependent theta oscillation
Caroline Haimerl1,2, David Angulo-Garcia1,3, Alessandro Torcini1,3,4, Rosa
Cossart1, Arnaud Malvache1
1
Institut de Neurobiologie de la Méditerrannée (INMED), INSERM,
UMR901, Aix-Marseille Univ, Marseille, France; 2Center of Neural Science,
New York University, New York, NY, USA; 3Aix-Marseille Univ, INSERM, INS,
Inst Neurosci Syst, Marseille, France; 4Laboratoire de Physique Théorique
et Modélisation, CNRS UMR 8089, Université de Cergy-Pontoise, F-95300
Cergy-Pontoise Cedex, France
Correspondence: Caroline Haimerl (david.angulo-garcia@univ-amu.fr)
BMC Neuroscience 2017, 18 (Suppl 1):P159
Recent studies have demonstrated the capacity of hippocampal sequences associated with theta oscillation, to encode
Page 96 of 176
spatio-temporal information. In particular, cells in CA1 become active
sequentially in a stable unidirectional order during spontaneous run
periods and under minimal external cues [1]. This sequential activity
seems to integrate either the distance that the animal has run or the
time that has elapsed, two related coding states that can be separated
through the change in cellular dynamics with the animals’ speed.
Other studies indicate that these cell sequences depend on theta
oscillation from the medial septum and may reflect input from CA3
[2–4].
Running speed of the animal has also shown to influence theta oscillation frequency and amplitude. This oscillation could thereby carry the
spatio-temporal information input required to determine distance/
time coding. Inspired by [2], we modeled a circular recurrent network
of excitatory cells with short-term synaptic plasticity [5] and global
inhibition. By applying speed-dependent theta oscillation, we reproduced the dynamics of spatio-temporal coding observed in experimental data and propose a mechanism of switching between the
two coding states through a change in integration of theta input. In
particular, our firing rate model reproduces the sequence properties
(recurrence, unidirectionality, sparse activity, memory) based on the
network characteristics of CA3 and allows exploring the dynamics of
the sequential activity. Simulations with this model show a non-trivial
relationship between sequence slope and the frequency/amplitude of
the oscillatory input: depending on the amplitude range of the theta
oscillation, sequence dynamics can either be independent of speed
(time coding) or linearly dependent on speed (distance coding). Therefore, the model proposes a network structure that could give rise to
two basic and possibly default, self-referenced coding states observed
in the hippocampus.
This model provides insights into how a recurrent network operates in
the absence of spatially specific input, but still allows for such input to
modulate sequential activity towards place field representation [2]. We
will next explore further the mechanisms of sequence generation and
coding correlates in both theoretical and experimental work.
References
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Hippocampal Sequences as a Population Template of Spatiotemporal
Information. Neuron 2015, 88(2):357–366.
2. Wang Y, Romani S, Lustig B, Leonardo A, Pastalkova E: Theta sequences are
essential for internally generated hippocampal firing fields. Nature Neuroscience 2015 18(2):282–290.
3. Salz DM., Tigany Z, Khasnabish S, Kohley A, Sheehan D, Howard MW,
Eichenbaum H: Time Cells in Hippocampal Area CA3.
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36:7476–7484.
4. Guzman SJ, Schlögl A, Frotscher M, Jonas P: Synaptic mechanisms of
pattern completion in the hippocampal CA3 network. Science 2016,
353:1117–1123.
5. Mongillo G, Barak, O, Tsodyks M: Synaptic theory of working memory. Science 2008, 319:1543–1546.
P160
The effect of progressive degradation of connectivity between brain
areas on the brain network structure
Kaoutar Skiker, Mounir Maouene
Department of mathematics and computer science, ENSAT, Abdelmalek
Essaadi’s University, Tangier, Morocco
Correspondence: Kaoutar Skiker (skiker.kaoutar85@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P160
Neurodegenerative diseases such as Alzheimer and Schizophrenia
are characterized by the progressive decline of cognitive functions
such as memory, language and consciousness with take the form of
memory loss, deficits in verbal and non-verbal communication and
so on. Cognitive deficits are interpreted in terms of damage in the
network of brain areas, instead of damage to specific brain areas [1].
Many studies combining network theory and neuroimaging data have
shown that brain networks, known to have a small world structure [2],
are disorganized in people with neurodegenerative diseases indicating that the connectivity between brain areas is altered by the disease
[1]. The disorganization of brain networks can be a consequence of the
BMC Neurosci 2017, 18(Suppl 1):60
vulnerability of hub areas to diseases or from the abnormal connectivity between brain areas.
In this paper, we assess how the progressive degradation of connectivity between brain areas affects the brain network structure. We propose an algorithm building on the idea that the connections between
brain areas are weakened as the disease progress in time. We apply
the algorithm on a functional connectivity matrix freely available for
download from the Brain Connectivity Toolbox consisting of nodes
representing brain areas and edges representing the functional links
between two brain areas [3]. The network is weighted, with weights
wij reflect the correlations between two brain areas Ai and Aj. At a
given threshold t, the new weights are given by wij-t; with t indicates
the progression of disease in time. The structure of the new network
is analyzed using graph theoretical measures including clustering
coefficient and path length. After damage, the functional brain network shows the properties of high clustering and low path length
indicting that the network presents a small world structure necessary
for the proper cognitive functioning. The progressive degradation of
links doesn’t change the network’s properties dramatically, clustering
coefficient are slightly modified until t = 0.25 (see Figure 1 for clustering coefficient). At this stage, the functional network shifts from high
organization to randomness.
In sum, cognitive deficits in neurodegenerative diseases can be understood in the scope of the progressive degradation of the connectivity
between brain areas within the network.
Page 97 of 176
of Salerno, 84084 Fisciano (SA), Italy; 2Department of Medicine, University
of Salerno, 84083 Lancusi (SA), Italy
Correspondence: Gianmarco Ragognetti (gragognetti@unisa.it)
BMC Neuroscience 2017, 18 (Suppl 1):P161
Neuro computational models represent a powerful tool for bridging the gap between functions of the neural circuits and observable
behaviors [1]. Once the model has been built, its output is compared
with the observations either to validate the model itself or to propose new hypotheses. This approach has led to building a multi-scale
model of the sensorimotor system from muscles, proprioceptors to
skeletal joints, spinal regulating centers and central control circuits
[2–6].
In this framework, we propose a neural network architecture to
simulate the selection of actions performed by the motor cortex in
response to a sensory input during a reward-based movement learning. The network has as many input nodes as the number of different
stimuli, each node being a combination of the sensory inputs, and as
many output nodes as the number of different actions that can be
performed, each node being a combination of the motor commands.
The network is fully connected, so that each stimulus concurs to the
selection of each action and each action is selected concurrently by
all the stimuli. The weights are updated by taking into account both
the expected reward and the actual reward, as suggested in [7]. By
adopting this architecture, the percept is represented by a combination of sensory inputs, while the action is represented by a combination of motor commands. Thus, it reproduces faithfully the condition
of experiments of motor learning when a set of sensory inputs, such
as semantically neutral visual stimuli, are presented to the subject
whose response is merely a motor action, such as pushing a button.
Under such conditions, it then becomes possible to fit the data provided by the experiments with the model to both estimate the validity of the model and to infer the role of the parameter on behavioral
traits.
The simulations were compared to the behaviors of human subjects
while learning which out of two buttons to press in response to a collection of visual stimuli containing edges and geometric shapes in a
reward based setting. The results showed that the behavior of the
complete system is the one expected under the hypothesis that the
reward acts by modulating the action selection triggered by the input
stimuli during motor learning. Moreover, differently from most literature models, the learning rate varies with the complexity of the task,
i.e. the number of input stimuli. It can be argued that the decrease in
learning rate seen in humans learning large set of stimuli could be due
to an attenuation of memory traces in real synapses over time. In our
future investigations, we will work to improve the model by adding
such an effect in our network.
References
Figure 1. The average clustering coefficient of the network
decreases following the progressive degradation of the connectivity between brain areas
References
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2. O Sporns: Network Attributes for Segregation and Integration in the
Human Brain. Current Opinion in Neurobiology 2013, 23: 162–71.
3. M Rubinov, O Sporns: Complex network measures of brain connectivity:
Uses and interpretations. Neuroimage 2010, 52:1059–1069.
P161
A network architecture for comparing the behavior of a
neurocomputational model of reward‑based learning with human
Gianmarco Ragognetti1, Letizia Lorusso2, Andrea Viggiano2 and Angelo
Marcelli1
1
Laboratory of Natural Computation, Department of Information
and Electrical Engineering and Applied Mathematics, University
1. Lan, N., Cheung, V. and Gandevia, S.C.: EDITORIAL - Neural and Computational Modeling of Movement Control. Front. in Comp. Neurosc. 2016, 10:
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BMC Neurosci 2017, 18(Suppl 1):60
P162
Distributed plasticity in the cerebellum: how do cerebellar cortex
and nuclei plasticity cooperate for learning?
Rosa Senatore, Antonio Parziale, Angelo Marcelli
Laboratory of Natural Computation, Department of Information
and Electrical Engineering and Applied Mathematics, University
of Salerno, 84084 Fisciano (SA), Italy
Correspondence: Rosa Senatore (rsenatore@unisa.it)
BMC Neuroscience 2017, 18 (Suppl 1):P162
Different forms of synaptic plasticity have been revealed within the cerebellum (CB), and many hypothesis about their role have been proposed [1].
We used a model-based analysis for investigating the role of these forms
of plasticity in three behaviors: phase reversal of the vestibule-ocular reflex,
acquisition of conditioned responses and learning a novel limb movement.
We investigated these behaviors since they involve different forms of learning: phase reversal requires to modify a preexistent stimulus-response (S-R)
association according to the feedback signal provided by climbing fibers
(CFs); conditioning involves learning a new S-R association according to a
preexistent one between the stimulus coming from the CFs and a motor
response; learning novel motor behaviors corresponds to create new S-R
associations according to the CF feedback. The analysis was carried through
a CB model that incorporates plasticity mechanisms at different stages
of the CB processing, both in cortex and nuclei [2]. Synaptic plasticity has
been simulated in both granular (Gr) and Purkinje (PC) network: granule
cells show intrinsic plasticity depending on mossy fibers (MFs) activity, and
MF-Gr synapses undergo both Long Term Depression (LTD) and Long Term
Potentiation (LTP)[3]; PF-PC synapses undergo both LTD and LTP, depending
on PF and CF activity [4]. The model also includes synaptic plasticity involving the molecular interneurons (MLI) at PF-MLI synapses [5] and Rebound
potentiation at MLI-PC synapses [6]. Within the CB nuclei, LTD occurs in
MF-NC synapses during inhibition from PCs, whereas LTP occurs during
release from inhibition [7]. Our results suggest that the main contribution to
CB learning is provided by the synaptic plasticity at PF-PC and MF-NC synapses. Indeed, excluding the plasticity at PF–PC site caused strong impairment in learning all the considered behaviors, while excluding the plasticity
at MF–NC site induced mild impairment in acquiring conditioned responses
and novel limb movements, and strong impairment was observed in phase
reversal and motor adaptation. Removal of other forms of synaptic plasticity only induced slower learning. Our results also suggest that LTP at PF-PC
underlies the extinction phenomenon observed in conditioning, and that
saving phenomenon could be ascribed to a residual plasticity within the CB
cortex rather than within the CB nucleus, since saving was observed even
after removal of MF-NC plasticity before reconditioning. Finally, model
simulations support the view that learned associations are transferred
from the CB cortex to the CB nuclei, due to the combined effect of plasticity at PF-PC synapses in early stage of learning, and MF-NC synapses in late
learning. Indeed, lesions at PCs layer or removal of PF-PC synaptic plasticity in late learning stage did not induced any impairment in the behavior
of the model, whereas removal of PF-PC synaptic plasticity in early learning
impaired learning capabilities of the model.
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cerebellar learning. Nat Rev Neurosci 2012, 13:619–635.
2. Senatore R, Parziale A, Marcelli A: A computational model for investigating
the role of cerebellum in acquisition and retention of motor behavior. 25th
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Page 98 of 176
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P163
Ising Model with conserved magnetization on the Human
Connectome: implications on the relation structure‑function
in wakefulness and anesthesia
S. Stramaglia1, M. Pellicoro1, L. Angelini1, E. Amico2,3, H. Aerts2, J. Cortés4, S.
Laureys3, D. Marinazzo2
1
Dipartimento di Fisica, Università degli Studi Aldo Moro, Bari, and INFN,
Sezione di Bari, Italy; 2Data Analysis Department, Ghent University, Ghent,
Belgium; 3Coma Science Group, University of Liège, Liège, Belgium;
4
Cruces Hospital and Ikerbasque Research Center, Bilbao, Spain
Correspondence: S. Stramaglia (sebastiano.stramaglia@ba.infn.it)
BMC Neuroscience 2017, 18 (Suppl 1):P163
Dynamical models implemented on the large-scale architecture of the
human brain may shed light on how function arises from the underlying structure. This is the case notably for simple abstract models, such
as the Ising one. We compare the spin correlations of the Ising model
and the empirical functional brain correlations, both at the single link
level and at the modular level, and show that the prediction is better
in anesthesia than in wakefulness, in agreement with recent experiments. We show that conserving the magnetization in the Ising model
dynamics (Kawasaki dynamics) leads to an improved prediction of the
empirical correlations in anesthetised brains, see Figure 1. Moreover,
we show that at the peak of the specific heat (the critical state) the spin
correlations are minimally shaped by the underlying structural network, explaining how the best match between structure and function is
obtained at the onset of criticality, as previously observed.
These findings could open the way to novel perspectives when the
conserved magnetization is interpreted in terms of a homeostatic
principle imposed to neural activity.
Figure 1. A. Mean Squared Error in Wakefulness and Anesthesia between the empirical connectivity and the one simulated by
Glauber and Kawasaki dynamics. B. Mutual Information between
the modular partitions of the empirical and modelled functional
networks. These quantities are depicted as a function of the
inverse temperature β
Conclusions: In agreement with recent theoretical frameworks [1], our
results suggest that a wide range of temperatures correspond to criticality of the dynamical Ising system on the connectome, rather than
a narrow interval centered in a critical state. In such conditions, the
correlational pattern is minimally shaped by the underlying structural
network. It follows that, assuming that the human brain operates close
to a critical regime [2], there is an intrinsic limitation in the relationship between structure and function that can be observed in data. We
show that empirical correlations among brain areas are better reproduced at the modular level using a model which conserves the global
magnetization. The most suitable way to compare functional and
structural patterns is to contrast them at the network level, using, e.g.,
the mutual information between partitions like in the present work.
References
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brain networks, Nature communications 2013, 4: 2521.
2. Chialvo D.: Emergent complex neural dynamics, Nature Physics 2010, 6:
744–750.
BMC Neurosci 2017, 18(Suppl 1):60
P164
Multiscale Granger causality analysis by à trous wavelet transform
S. Stramaglia1, I. Bassez2, L. Faes3, D. Marinazzo2
1
Dipartimento di Fisica, Università degli Studi Aldo Moro, Bari, and INFN,
Sezione di Bari, Italy; 2Data Analysis Department, Ghent University, Ghent,
Belgium; 3BIOtech, Dept. of Industrial Engineering, University of Trento,
and IRCS-PAT FBK, Trento, Italy
Correspondence: S. Stramaglia (sebastiano.stramaglia@ba.infn.it)
BMC Neuroscience 2017, 18 (Suppl 1):P164
Great attention has been devoted in the last years to the identification
of information flows in human brains. Since interactions occur across
multiple temporal scales, it is likely that information flow will exhibit a
multiscale structure: high-frequency activity, reflecting local domains
of cortical processing, and low-frequency activity dynamically spread
across the brain regions by both external sensory input and internal cognitive events. In order to detect information flow at multiple
scale the decomposition of the signals in the wavelet space has been
proposed in [1]; an analytical frame for linear multivariate stochastic
processes explored at different time scales has been proposed in [2].
However, the computation of multiscale measures of information
dynamics may be complicated by theoretical and practical issues
such as filtering and undersampling: to overcome this problems, we
propose here another wavelet-based approach for multiscale causality analysis, which is characterized by the following properties: (i) only
the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the à trous wavelet transform with cubic
B-spline filter [3]. The use of the à trous transform is suggested by its
interesting properties, indeed it satisfies the shift invariance, and its
coefficients at time t are a linear combination of the time series values;
no decimation of the time series, as in the discrete wavelet transform,
is done. Granger causality examines how much the predictability of
the target from its past improves when the driver variables’ past values
are included in the regression, where m is the order of the model. We
propose here to measure the causality at scale s by including w(t-1,s),
w(t-2,s),…,w(t-m,s) in the regression model of the target, where w(t,s)
are the à trous wavelet coefficients of the driver. In figure 1 we depict
the multiscale causality evaluated by the proposed approach on a simulated two-dimensional linear system unidirectionally coupled with
lag equal to 8 and strength a: it increases with the strength and peaks
in correspondence of the lag. We have applied the proposed algorithm to scalp EEG signals [4], and we found that the global amount
of causality among signals is significantly decreasing as the scale s is
increased. Furthermore, comparing signals corresponding to resting conditions with closed eyes and with open eyes, we found that at
large scales the effective connectivity, in terms of the proposed measure, is significantly lower with eyes open.
Page 99 of 176
3. Renaud O, Starck, J-L, Murtagh, F: Wavelet-Based Combined Signal Filtering
and Prediction. IEEE Transactions on Systems, Man, and Cybernetics, Part B:
Cybernetics. 2005, vol. 35, no. 6, p. 1241–1251
4. http://www.physionet.org/pn4/eegmmidb
P165
New (spectral) dynamic causal modeling scheme improves
effective connectivity estimation within resting state networks
in longitudinal data
Hannes Almgren1, Frederik Van De Steen1, Adeel Razi2,3, Daniele
Marinazzo1
1
Department of Data Analysis, Ghent University, Ghent, 9000, Belgium;
2
The Wellcome Trust Centre for Neuroimaging, University College
London, London, WC1 N 3BG, UK; 3Department of Electronic Engineering,
NED University of Engineering and Technology, Karachi, Pakistan
Correspondence: Hannes Almgren (Hannes.Almgren@ugent.be)
BMC Neuroscience 2017, 18 (Suppl 1):P165
Effective connectivity within resting state networks has been estimated using spectral dynamic causal modeling (spDCM) [1]. Since
its initial release, spDCM has been updated to improve performance
and to render it applicable to larger networks. The objective of the
present study is to assess the impact of these changes on parameter estimates and stability. We therefore compared performance
between an early version of DCM (v6303) and a newer version of
DCM (v6801) in combination with the parametric empirical Bayesian (PEB) framework [2]. Both were compared regarding (1) ability to explain observed cross spectral densities (CSD), (2) estimated
network structure, and (3) stability of parameter estimates. An
extensive single-subject longitudinal dataset, including 101 resting
state fMRI sessions, was analyzed (myconnectome.org/wp) [3]. Eight
resting state sessions were chosen for our analyses: occipital and
lateral visual, auditory, somatomotor, left and right frontoparietal,
default mode, and executive control network. Results showed that
the newer spDCM-PEB combination explained the data (i.e., CSDs)
far better than the older spDCM (95.31% versus 68.31% explained
variance, respectively). Furthermore, the older version often failed
to yield proper estimates (i.e., because of low proportion explained
variance or estimated connection strengths near zero) in networks
consisting of two- or three regions, while the newer version showed
less such problems. Concerning average network structure across
sessions, the newer spDCM-PEB combination detected asymmetric
influences within networks consisting of two regions (see Figure 1).
Furthermore, regions located in the medial part of the brain showed
larger in- versus out-connectivity. For the default mode network,
consisting of four regions in the present study, both versions yielded
largely similar network structures (i.e., reciprocal influences between
bilateral parietal cortices, and larger in- versus out-connectivity for
medial areas). However, the older version of spDCM showed a positive influence (0.21 Hz) from precuneus to medial prefrontal cortex,
which was much smaller (0.05 Hz) for the newer DCM-PEB combination. Stability depended profoundly on the size of the network:
parameter estimates showed higher stability in two-region networks
than in larger networks for both versions.
Figure 1. A. Granger causality in an unidirectionally coupled system is depicted as a function of the scale for several values of the
coupling. B. GC values for eyes open and closed conditions from
regular time series. C. GC values in the same conditions from wavelet coefficients (scale 4)
References
1. Lungarella M, Pitti A, Kuniyoshi K: Information transfer at multiple scales.
Phys. Rev. E 2007, 76: 056117
2. Faes, L., Montalto, A., Stramaglia, S., Nollo, G., Marinazzo, D.: Multiscale
analysis of information dynamics for linear multivariate processes, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine
and Biology Society, EMBS 2016.
Figure 1. Comparison of posterior parameter estimates within the
auditory network. A. median posterior parameter estimates for the
older version (shown in red) and the newer spDCM-PEB combination (shown in black). B and C. distribution of these parameter
estimates over sessions, together with the bootstrapped high density intervals, for both the older and newer scheme
BMC Neurosci 2017, 18(Suppl 1):60
Page 100 of 176
References
1. Friston KJ, Kahan J, Biswal B, Razi, A: A DCM for resting state fMRI. NeuroImage 2014, 94:396–407.
2. Friston KJ, Litvak V, Oswal A, Razi A, Stephan KE, van Wijk BC, Ziegler G,
Zeidman P: Bayesian model reduction and empirical Bayes for group (DCM)
studies. NeuroImage 2016, 128:413–431.
3. Laumann TO, Gordon EM, Adeyemo B, Snyder AZ, Joo SJ, Chen MY, Gilmore
AW, McDermott KB, Nelson SM, Dosenbach NU, et al.: Functional system
and areal organization of a highly sampled individual human brain. Neuron
2015, 87(3):657–670.
P166
Effective connectivity modulations of win‑and loss feedback:
A dynamic causal modeling study of the human connectome
gambling task
Frederik Van de Steen1, Ruth Krebs2, Daniele Marinazzo1
1
Department of data analysis, Ghent University, Ghent, 9000, Belgium;
2
Department of experimental psychology, Ghent University, Ghent, 9000,
Belgium
Correspondence: Frederik Van de Steen (frederik.vandesteen@ugent.be)
BMC Neuroscience 2017, 18 (Suppl 1):P166
The main goal of this study was to investigate changes in effective
connectivity associated with reward and punishment. More specifically, changes in connectivity between the ventral striatum (VS), anterior insula (aI), anterior cingulate cortex (ACC) and occipital cortex
(OCC) that are related to win- and loss- feedback were studied.
Here, fMRI data from the human connectome project [1] was used
for our study purposes. Data from 369 unrelated subjects performing a gambling task was analyzed. In short, participants played a
card game where they had to guess whether the upcoming card
would be higher or less than 5 (range was between 1 and 9). After
the gamble, feedback was provided indicating a reward, punishment or neutral trial. The minimally preprocessed data was used
and extra spatially smoothed with a 5-mm FWHM Gaussian kernel.
The images were then entered in a first level general linear model
(GLM) and summary statistic images of the first level GLM were
entered in a second level GLM. The following two contrasts were
used to identify the relevant brain regions at the group level: [Win
- Neut] AND [Loss-Neut] (i.e. conjunction), and [Win-neut]. Based
on the group level results, time-series of VS, aI, ACC and OCC were
extracted for every subject and used in further dynamic causal
modeling (DCM, [2]) analysis. We specified a fully connected model
(i.e. all nodes are reciprocally connected) where the win and loss
events were allowed to modulate all connections. The driving input
consisted of all feedback events (win, loss and neutral events) and
entered the DCM’s via OCC. The fully connected model was estimated for every subject and then used in the recently proposed
parametric empirical Bayesian (PEB, [3]) framework for estimating DCM parameters at the group level. Finally, we used Bayesian
model reduction to obtain the best 255 nested models. Since there
was no clear winning model, Bayesian model averaging (BMA) of
the 256 model (full + 255 nested models) parameters was performed. Figure 1. shows the group level BMA modulatory parameters with a posterior probability >.95.
Conclusion: Overall, both win- and loss- feedback have a general increasing effect on effective connectivity. The main difference
between win and loss can be observed for the connection from aI and
OCC with loss-feedback having a decreased effect. In addition, only
win-feedback increases the connection from VS to aI. Overall, the VS
appears as a key region in conveying loss and win information across
the network.
Figure 1. BMA modulatory parameters at the group level are
shown for A. loss feedback; B. win feedback
Acknowledgements
This research was supported by the Fund for Scientific Research-Flanders (FWO-V), Grant FWO16/ASP_H/255.
References
1. Van Essen, D. et al. The WU-Minn Human Connectome Project: An overview.
NeuroImage, 2013, 80: 62–79.
2. Friston, Karl J., Lee Harrison, and Will Penny. Dynamic causal modelling.
Neuroimage, 2003, 19(4): 1273–1302.
3. Friston, Karl J., et al. Bayesian model reduction and empirical Bayes for
group (DCM) studies. Neuroimage
P167
Modeling global brain dynamics in brain tumor patients using the
Virtual Brain
Hannelore Aerts, Daniele Marinazzo
Department of Data Analysis, Ghent University, Ghent, Belgium
Correspondence: Hannelore Aerts (hannelore.aerts@ugent.be)
BMC Neuroscience 2017, 18 (Suppl 1):P167
Increasingly, computational models of brain activity are applied to
investigate the relation between structure and function. In addition,
biologically interpretable dynamical models may be used as unique
predictive tools to investigate the impact of structural connectivity
damage on brain dynamics. That is, individually modeled biophysical
parameters could inform on alterations in patients’ local and largescale brain dynamics, which are invisible to brain-imaging devices.
In this study, we compared global biophysical model parameters
between brain tumor patients and healthy controls. To this end, we
used The Virtual Brain (TVB; [1]), a neuroinformatics platform that utilizes empirical structural connectivity data to create dynamic models
of an individual’s brain.
Ten glioma patients (WHO grade II and III, mean age 41.1yo, 4 females;
5 from open access dataset [2]), 13 meningioma patients (mean age
60.23y, 11 females), three pseudo-meningioma patients (subtentorial brain tumors, mean age 58yo, 2 females) and 11 healthy partners
(mean age 58.6y, 4 females) were included in this study. From all participants, diffusion MRI, resting-state fMRI and T1-weighted MRI data
were acquired. Data were preprocessed and converted to a subjectspecific structural and functional connectivity matrix using a modified
version of the TVB preprocessing pipeline [3].
BMC Neurosci 2017, 18(Suppl 1):60
Page 101 of 176
In order to simulate brain dynamics, the reduced Wong-Wang model
[4] was used. This is a dynamical mean field model that consistently
summarizes the realistic dynamics of a detailed spiking and conductance-based synaptic large-scale network. A subject-specific parameter
space exploration was conducted to obtain an optimal correspondence between the individual’s simulated and empirical functional
connectivity matrix. To this end, values of the global scaling factor G
and the local feedback inhibitory synaptic coupling Ji were varied. Values of G and Ji yielding optimal correspondence were then compared
between the brain tumor patient groups and healthy controls.
The distribution of optimal values for G and Ji per group is depicted
in Figure 1. Visually, no clear group differences are apparent. In future
studies, larger sample sizes will be utilized, as data collection is still
ongoing and more efforts to data sharing across labs are undertaken.
In addition, local model parameter alterations in the vicinity of the
lesion will be examined, since global model parameters might not be
sufficiently sensitive to capture local lesion effects.
Mind Institute, École polytechnique fédérale de Lausanne, Lausanne,
Switzerland; 4Institute of Mathematics, University of Aberdeen, Aberdeen,
Scotland, UK; 5Department of Integrative Neurophysiology, Center
for Neurogenomics and Cognitive Research, VU Universiteit Amsterdam,
Amsterdam, the Netherlands
Correspondence: Lida Kanari (lida.kanari@epfl.ch)
BMC Neuroscience 2017, 18 (Suppl 1):P168
Figure 1. Distribution of optimal model parameter values per
group: control subjects (CON), pseudo control subjects with subtentorial brain tumor (pCON), meningioma patients (MEN), and
glioma WHO grade II and III patients (GLI). A. Global scaling factor
(G); B. Local feedback inhibitory synaptic coupling (Ji)
Figure 1. Topological morphology descriptor. A. The neuronal tree
is mapped into a barcode. B. Each bar represents the lifetime of a
branch; its start and end distance from the soma
References
1. P Sanz Leon, S A Knock, M M Woodman, L Domide, J Mersmann, A R
McIntosh, V K Jirsa. The Virtual Brain: A simulator of primate brain network
dynamics. Frontiers in Neuroinformatics 2013, 7:1–23.
2. C Pernet, K Gorgolewski, I Whittle. UK Data Archive. [http://dx.doi.
org/10.5255/UKDA-SN-851861]
3. M Schirner, S Rothmeier, V K Jirsa, A R McIntosh, P Ritter. An automated
pipeline for constructing personalized virtual brains from multimodal
neuroimaging data. NeuroImage 2015, 117:343–357.
4. G Deco, A Ponce-Alvarez, P Hagmann, G L Romani, D Martini, M Corbetta.
How local excitation-inhibition ratio impacts the whole brain dynamics.
The Journal of Neuroscience 2014, 34:7886–7898.
P168
Representation of Neuronal Morphologies
Lida Kanari1, Pawel Dlotko2, Martina Scolamiero3, Ran Levi4, Julian
Shillcock1, Christiaan P.J. de Kock5, Kathryn Hess3 and Henry Markram1
1
Blue Brain Project, École polytechnique fédérale de Lausanne, Lausanne,
Switzerland; 2Departement of Mathematics, Swansea University, Swansea,
Wales, UK; 3Laboratory for Topology and Neuroscience at the Brain
The shape of neuronal arborizations defines amongst other aspects
their physical connectivity and functionality. Yet an efficient method
for quantitatively analyzing the spatial structure of such trees has been
difficult to establish. The wide diversity of neuronal morphologies in
the brain, even for cells identified by experts as of the same type, renders an objective classification scheme a challenging task.
We propose a Topological Morphology Descriptor [1], inspired by
Topological Data Analysis, to quantitatively analyze the branching
shapes of neurons, which overcomes the limitations of existing techniques. The TMD algorithm maps the branches of a tree (Fig 1A) into a
“barcode” (Fig 1B). The TMD encodes the morphology of the tree into
a simplified topological representation that preserves sufficient information to be useful for the comparison and the distinction of different
branching patterns.
This method is applicable to any tree-like structure, and we demonstrate its generality by applying it to groups of mathematical random
trees and neuronal morphologies. We identify the structural differences between known morphological types [2-3] as well as subtypes
for human temporal cortex L2/3 pyramidal cells [4]. Our results show
that the TMD of tree shapes reliably and efficiently distinguishes different shapes of trees and neurons. Therefore, the TMD provides an
objective benchmark test of the quality of any grouping of branching
trees into discrete morphological classes. Our results demonstrate that
the TMD can enhance our understanding of the anatomy of neuronal
morphologies.
References
1. Kanari L, Dłotko P, Scolamiero M, Levi R, Shillcock J, Hess K, Markram H,
Quantifying topological invariants of neuronal morphologies, arxiv.org
2016, [https://arxiv.org/abs/1603.08432]
2. Ascoli G.A., Donohue D.E. and Halavi M., NeuroMorpho.Org: A Central
Resource for Neuronal Morphologies, J. Neurosc. 2007, 27 (35): 9247–9251.
3. Markram H. Muller E., Ramaswamy S., Reimann M.W. et al., Reconstruction
and Simulation of Neocortical Microcircuitry, Cell 2015, 163 (2): 456–492.
4. Mohan H., de Kock C.P.J., et al. Dendritic and Axonal Architecture of Individual Pyramidal Neurons across Layers of Adult Human Neocortex, Cereb
Cortex 2015, 25 (12): 4839–4853.
BMC Neurosci 2017, 18(Suppl 1):60
P169
Firing Rate Heterogeneity and Consequences for Stimulus
Estimation in the Electrosensory System
Cheng Ly1, Gary Marsat2
1
Department of Statistical Sciences and Operations Research, Virginia
Commonwealth University, Richmond, VA 23284, USA; 2Biology
Department, West Virginia University, Morgantown, WV 26506, USA
Correspondence: Cheng Ly (CLy@vcu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P169
Heterogeneity of neural attributes is recognized as a crucial feature
in neural processing. Thus, we have developed theoretical methods
(based on [1]) to characterize the firing rate distribution of spiking
neural networks with intrinsic and network heterogeneity [2], both
of which have been widely reported in experiments. This relationship
(intrinsic and network) can lead to various levels of firing rate heterogeneity, depending on regime.
Next we adapt our theory to a delayed feedforward spiking network
model of the electrosensory system of the weakly electric fish. Experimental recordings indicate that feedforward network input can mediate response heterogeneity of pyramidal cells [3]. We demonstrate
that structured connectivity rules, derived from our theory, can lead
to qualitatively similar statistics as the experimental data. Thus, the
model demonstrates that intrinsic and network attributes do not interact in a linear manner but rather in a complex stimulus-dependent
fashion to increase or decrease neural heterogeneity and thus shape
population codes.
As evidence for heterogeneity shaping population codes, we also present some preliminary work using recordings from electric fish subject
to noisy stimuli. We use a GLM model for each neuron, fit the parameters to the data using standard maximum likelihood methods, and
perform Bayesian estimation of the stimuli. We find that firing rate heterogeneity is a signature of optimal (Bayesian) stimulus estimation of
noisy stimuli. Interestingly, the firing rate correlation is not an indicator
of decoding performance for a given population of neurons.
References
1. W. Nicola, C. Ly, S.A. Campbell: One-Dimensional Population Density
Approaches to Recurrently Coupled Networks of Neurons with Noise. SIAM
Journal on Applied Mathematics 2015, 75:2333–2360.
2. C. Ly: Firing Rate Dynamics in Recurrent Spiking Neural Networks with
Intrinsic and Network Heterogeneity. Journal of Computational Neuroscience 2015, 39:311–327.
3. G. Marsat, G.J. Hupe, K.M. Allen: Heterogeneous response properties in a
population of sensory neurons are structured to efficiently code naturalistic stimuli. Program # 181.20 Neuroscience Meeting Planner 2014.
P170
Knowledge Space: a community encyclopedia linking brain
research concepts to data, models and literature
Tom Gillespie3, Willy Wong3, Malin Sandström1, Mathew Abrams1, Jeffrey
S. Grethe3, Maryann Martone4
1
INCF Secretariat, Karolinska Institute, Nobels väg 15A, 17177 Stockholm,
Sweden; 2Campus Biotech, EPFL, CH-1202 Genève, Switzerland; 3Center
for Research in Biological Systems, UCSD, La Jolla 92093, CA, USA;
4
Neurosciences, UCSD, La Jolla 92093, CA, USA
Correspondence: Malin Sandström (malin.sandstrom@incf.org)
BMC Neuroscience 2017, 18 (Suppl 1):P170
KnowledgeSpace [1] is a community encyclopedia platform currently
under development where neuroscience data and knowledge are synthesized. KnowledgeSpace aims to provide a global interface between
current brain research concepts and the data, models and literature
about them. It is an open project that welcomes participation and contributions from members of the global research community.
KnowledgeSpace version 1.0 was launched at Neuroscience 2016 in
San Diego, November 12-16, with three modes of search - keyword,
category and atlas-based (so far only for mouse brain). During the
pre-launch phase, work focused on linking concepts to data, models, and literature from existing community resources. Current data
sources include NeuroLex, Allen Institute for Brain Sciences, The Blue
Page 102 of 176
Brain Project, NeuroMorpho, NeuroElectro, Cell Image Library, NIF Integrated Connectivity, Ion Channel Genealogy, ModelDB, Open Source
Brain, GenSat, BrainMaps, NeuronDB, The Human Brain Atlas, and PubMed. Initial content included in KnowledgeSpace covers ion channels,
neuron types, and microcircuitry. For each content type, physiology,
gene expression, anatomy, models, and morphology data sources are
available.
Going forward we will enhance atlas representations of the mouse
brain linking concepts to data, models, and literature, and an atlas
representation of the human brain that links to available data, models, and literature will be implemented. Links to analysis tools will also
be integrated into the KnowledgeSpace data section. The project
will also develop protocols, standards, and mechanisms that allow
the community to add data, analysis tools, and model content to
KnowledgeSpace.
The initial development of KnowledgeSpace has been driven and
supported by the International Neuroinformatics Coordinating Facility (INCF; incf.org), the Neuroscience Information Framework (NIF;
neuinfo.org) and the Blue Brain Project (BBP; bluebrain.epfl.ch). The
KnowledgeSpace also represents an important component of the
Neuroinformatics Platform being deployed in the Human Brain Project web portal. KnowledgeSpace is currently transitioning to a shared
governance model, with a Governing Board composed of members of
the neuroscience community who are currently funded to generate or
share data and/or code as part of a lab, project or organization, and
who will rotate off the board when their project ends.
Reference
1. KnowledgeSpace website [https://knowledge-space.org/index.html]
P171
Evaluating the computational capacity of a cerebellum model
Robin De Gernier1, Sergio Solinas2, Christian Rössert3, Marc Haelterman1,
Serge Massar1
1
École polytechnique de Bruxelles, Université libre de Bruxelles, Brussels,
Belgium, 1050; 2Department of Biomedical Science, University of Sassari,
Sassari, Italia, 07100; 3Blue Brain Project, École polytechnique fédérale de
Lausanne, Geneva, CH-1202, Switzerland
Correspondence: Robin De Gernier (rdegerni@ulb.ac.be)
BMC Neuroscience 2017, 18 (Suppl 1):P171
The cerebellum plays an essential role in tasks ranging from motor
control to higher cognitive functions (such as language processing)
and receives input from many brain areas. A general framework for
understanding cerebellar function is to view it as an adaptive-filter
[1]. Within this framework, understanding, from computational and
experimental studies, how the cerebellum processes information and
what kind of computations it performs is a complex task, yet to be
fully accomplished. In the case of computational studies, this reflects
a need for new systematic methods to characterize the computational
capacities of cerebellum models. In the present work, to fulfill this
need, we apply a method borrowed from the field of machine learning to evaluate the computational capacity of a prototypical model of
the cerebellum cortical network. Using this method, we find that the
model can perform both linear operations on input signals –which is
expected from previous work-, and –more surprisingly- highly nonlinear operations on input signals.
The model that we study is a simple rate model of the cerebellar
granular layer in which granule cells inhibit each other via a singleexponential synaptic connection. The resulting recurrent inhibition is
an abstraction of the inhibitory feedback circuit composed of granule
and Golgi cells. Purkinje cells are modelled as linear trainable readout
neurons. The model was originally introduced in [2, 3] to demonstrate
that models of the cerebellum that include recurrence in the granular
layer are suited for timing-related tasks. Further studies carried out in
[4] showed how the recurrent dynamics of the network can provide
the basis for constructing temporal filters.
The method, described in detail in [5], and developed in the context
of the artificial intelligence algorithm known as reservoir computing [6], consists in feeding the network model with a random time
dependent input signal and then quantifying how well a complete
BMC Neurosci 2017, 18(Suppl 1):60
set of functions (each function representing a different type of computation) of the input signal can be reconstructed by taking a linear
combination of the neuronal activations. The result is a quantitative
estimate of the number of different computations that can be carried out by the model. We conducted simulations with 1000 granule
cells. Our results show that the cerebellum prototypical model has
the capability to compute both linear and highly nonlinear functions
of its input. Specifically, the model is able to reconstruct Legendre
polynomial functions up to the 10th degree. Moreover, the model can
internally maintain a delayed representation of the input with delays
of up to 100 ms, and perform operations on that delayed representation. Despite their abstract nature, these two properties are essential
to perform typical cerebellar functions, such as learning the timing of
conditioned reflexes or fine-tuning nonlinear motor control tasks or,
we believe, even higher cognitive functions.
In future work, we hope to confirm these abstract results by applying
our cerebellum model to typical cerebellar tasks. Additionally, we will
compare our results with a very recent work which studied how a model
of the cerebellum could solve several machine learning tasks [7].
References
1. Dean P, Porril J: The cerebellar microcircuit as an adaptive filter: experimental
and computational evidence. Nat Rev Neurosci 2010, 11(1): 30–43.
2. Yamazaki T, Tanaka S: Neural Modeling of an Internal Clock. Neural Comput
2005, 17(5): 1032–1058.
3. Yamazaki T, Tanaka S: The cerebellum as a liquid state machine. Neural Netw
2007, 20(3): 290–297.
4. Rössert C, Dean P, Porrill J: At the Edge of Chaos: How Cerebellar Granular
Layer Network Dynamics Can Provide the Basis for Temporal Filters. PLOS
Comput Biol 2015, 11(10):e1004515.
5. Dambre J, Verstraeten D, Schrauwen B, Massar S: Information processing
capacity of dynamical systems. Sci Rep 2012, 2:514.
6. Lukoševičius M, Jaeger H: Reservoir computing approaches to recurrent
neural network training. Computer Science Review 2009, 3:127–149.
7. Hausknecht M, Li WK, Mauk M, Stone P: Machine Learning Capabilities of a Simulated Cerebellum. IEEE Trans Neural Netw Learn Syst 2017,
28(3):510–522.
P172
Complexity of cortical connectivity promotes self‑organized
criticality
Valentina Pasquale1, Vito Paolo Pastore2, Sergio Martinoia2, Paolo
Massobrio2
1
Neuroscience and Brain Technologies Department, Istituto Italiano
di Tecnologia (IIT), Genova, Italy; 2Department of Informatics,
Bioengineering, Robotics, System Engineering (DIBRIS), University
of Genova, Genova, Italy
Correspondence: Valentina Pasquale (valentina.pasquale@iit.it)
BMC Neuroscience 2017, 18 (Suppl 1):P172
Large-scale in vitro cortical networks spontaneously exhibit recurrent
events of propagating spiking and bursting activity, usually termed as
neuronal avalanches, since their size (and lifetime) distribution can be
approximated by a power law, as in critical sand pile models [1, 2] (Figure 1). However, neuronal avalanches in cultures of dissociated cortical neurons can distribute according to three different dynamic states,
namely sub-critical, critical, or super-critical, depending on several
factors like developmental stage, excitation/inhibition balance, cell
density, etc. [3]. In this work, we investigated the role of connectivity
in driving spontaneous activity towards critical, sub-critical or supercritical regimes, by combining both experimental and computational
investigations.
Our experimental model consists of mature networks (third week
of in vitro development) of cortical dissociated neurons coupled to
High-Density Micro-Electrode Arrays (HD-MEAs) (3Brain, Wadenswill, Switzerland). These devices, containing 4’096 microelectrodes,
81 µm-spaced, allow to follow the emergence and propagation of
neuronal avalanches with high spatio-temporal resolution. We estimated the functional connectivity of cortical networks by using crosscorrelation based methods, collected in the software TOOLCONNECT [4].
In particular, our cross-correlation algorithm is able to reliably and
Page 103 of 176
accurately infer functional and effective excitatory and inhibitory links
in ex vivo neuronal networks, while guaranteeing high computational
performances necessary to process large-scale population recordings.
To support our experimental investigations, we also developed a computational model of neuronal network, made up of Izhikevich neurons
[5] structurally connected by following well defined topologies of connectivity (e.g., random, scale-free, small-world).
Simulations of the model demonstrated that the presence of hubs, the
physiological balance between excitation and inhibition, and the concurrent presence of scale-free and small-world features are necessary
to induce critical dynamics. We then confirmed the predictions of the
model by analyzing medium/high density cortical cultures coupled to
HD-MEAs, finding that networks featuring both scale-free and smallworld properties (as computed from functional connectivity graphs)
display critical behavior.
Figure 1. Example of electrophysiological activity of a cortical network coupled to a High-Density Micro-Electrode Arrays (HD-MEAs)
References
1. Beggs JM, Plenz D: Neuronal avalanches in neocortical circuits. J Neurosci
2003, 23(35):11167–11177.
2. Bak P: How nature works. Oxford (UK): Oxford University Press; 1997.
3. Pasquale V, Massobrio P, Bologna LL, Chiappalone M, Martinoia S: Selforganization and neuronal avalanches in networks of dissociated cortical
neurons. Neuroscience 2008, 153(4):1354–1369.
4. Pastore VP, Poli D, Godjoski A, Martinoia S, Massobrio P: ToolConnect: a
functional connectivity toolbox for in vitro networks. Front Neuroinform
2016, 10(13).
5. Izhikevich EM: Simple model of spiking neurons. IEEE Trans Neur Net 2003,
14:1569–1572.
P173
Attractor dynamics of cortical assemblies underlying brain
awakening from deep anesthesia
Cristiano Capone1,2, Núria Tort-Colet3, Maria V. Sanchez-Vives3,4, Maurizio
Mattia1
1
Istituto Superiore di Sanità (ISS), 00161 Rome, Italy; 2PhD Program
in Physics, Sapienza University, 00185 Rome, Italy; 3Institut
d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036
Barcelona, Spain; 4Institució Catalana de Recerca i Estudis Avançats
(ICREA), 08010 Barcelona, Spain
Correspondence: Cristiano Capone (cristiano0capone@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P173
Slow rhythms of activity (~1 Hz) and slow-wave activity [1, 2] are a
remarkably reproducible dynamical activity pattern with a low degree
of complexity which opens a window on the brain multiscale organization, on top of which cognitive functions emerge during wakefulness.
Understanding how such transition takes place might shade light on
the emergence of the rich repertoire of neuronal dynamics underlying
brain computation. Sleep-wake transition is a widely-studied phenomenon ranging in experimental, computational and theoretical frameworks [3–5], however it is still debated how brain state changes occur.
In our previous work [6] we showed from intracortical recordings in
anesthetized rats, that sleep-like rhythms fade out when wakefulness
BMC Neurosci 2017, 18(Suppl 1):60
is approached giving rise to an alternation between slow Up/Down
oscillations and awake-like (AL) activity periods. We also shown how
this phase of activity pattern bistability is captured by a mean-field
rate-based model of a cortical column. Guided by this mean-field
model, spiking neuron networks are devised to reproduce the electrophysiological changes displayed during the transition. Also, the model
gave us hints on the mechanistic and dynamical nature of the patterns
of activity observed, suggesting that the AL periods appearance is due
to a Hopf-like transition from a limit cycle to a stable fixed point at a
high level of activity, and that AL-SO alternation is related to the presence of a slow oscillating (∼ 0.2 Hz) level of excitation probably due to
populations of neurons in deeper regions of the brain.
We extended our previous findings by performing a stability analysis of the competing attractors, observing a modulation of their stability, that affect the dynamics of the Down-to-AL transition and the
residence dynamics within the AL state. Moreover, we found that
the mean-field model remarkably matches the stability modulation
observed in experiments. This match between theory and experiments further strengthens our claim that cortical assemblies of neurons display a Hopf bifurcation when anesthesia fades out.
Such observation gives important information on intrinsic dynamical properties of the system, suggesting that it does not respond in a
passive way but rather it is a strongly nonlinear component, capable
to drastically change its dynamics under small changes of relevant
parameters. This can provide a computational advantage in terms of
the capability of producing a rich repertoire of network states during
wakefulness.
Acknowledgements
Supported by EC FET Flagship HBP SGA1 (720270) to MM and MVSV
References
1. Sanchez-Vives MV, & Mattia M: Slow wave activity as the default mode of the
cerebral cortex. Arch Ital Biol 2014, 152:147–155.
2. Capone Cristiano, Mattia Maurizio: Speed hysteresis and noise shaping of
traveling fronts in neural fields: role of local circuitry and nonlocal connectivity. Scientific Reports 2016, 7:39611 doi: 10.1038/srep39611
3. Bettinardi RG, Tort-Colet N, Ruiz-Mejias M, Sanchez-Vives MV, & Deco G:
Gradual emergence of spontaneous correlated brain activity during fading
of general anesthesia in rats: evidences from fMRI and local field potentials.
Neuroimage 2015, 114:185–198.
4. G. Deco, P. Hagmann, A. G. Hudetz, and G. Tononi: Modeling resting-stat
state functional networks when the cortex falls asleep: local and global
changes., Cereb. Cortex 2014, vol. 24, no. 12, pp. 3180–3194.
5. Steyn-Ross ML, Steyn-Ross DA, and Sleigh JW: Interacting Turing-Hopf
instabilities drive symmetry-breaking transitions in a mean-field model of
the cortex: a mechanism for the slow oscillation, Phys. Rev. X, vol. 3, no. 2, p.
21005, 2013.
6. Capone C, Tort-Colet N, Mattia M, Sanchez-Vives MV (2016) Multistable
attractor dynamics in columnar cortical networks transitioning from deep
anesthesia to wakefulness. Bernstein Conference 2016.
P174
Are receptive fields in visual cortex quantitatively consistent
with efficient coding?
Ali Almasi1,2, Shaun L. Cloherty4, David B. Grayden2, Yan T. Wong3,4,
Michael R. Ibbotson1,5, Hamish Meffin1,5
1
National Vision Research Institute, Australian College of Optometry,
Melbourne, Australia; 2NeuroEngineering Laboratory, Dept. Biomedical
Eng., University of Melbourne, Melbourne, Australia; 3Dept. of Physiology,
Monash University, Melbourne, Australia; 4Dept. of Electrical & Computer
Systems Eng., Monash University, Melbourne, Australia; 5ARC Centre
of Excellence for Integrative Brain Function, University of Melbourne,
Melbourne, Australia
Correspondence: Hamish Meffin (hmeffin@unimelb.edu.au)
BMC Neuroscience 2017, 18 (Suppl 1):P174
Numerous studies, across different sensory modalities, suggest that
the neural code employed in early stages of the cortical hierarchy can
be explained in terms of Efficient Coding. This principle states that
information is represented in a neural population so as to minimize
redundancy. This is achieved when the features to which neurons
Page 104 of 176
are tuned occur in a statistically independent fashion in the sensory
environment. The “statistically independent features” can be rigorously identified through methods of statistical inference, and can be
associated with a cell’s receptive field (RF). Several studies using these
methods have shown a qualitative similarity between predicted RFs
and those found in primary visual cortex, for simple and complex cells
(with linear and non-linear RF structures, respectively).
Recent methods allow direct experimental estimation of RFs. Using
these methods, we report on the first quantitative evaluation of the
Efficient Coding Hypothesis at the level of RF structures, including
both simple and complex cells.
Experimental RF structures were estimated from recordings of singleunits in the primary visual cortex of anaesthetized cats in response
to presentation of Gaussian white noise. RFs were estimated from
recordings assuming a General Quadratic Model for spike rate and
performing maximum likelihood estimation on the response given the
stimulus. Theoretical Efficient Coding RF structures were inferred by
performing unsupervised learning on a set of natural images, under
the assumption of Efficient Coding that evoked spike rates were statistically independent and sparsely distributed, and using the same General Quadratic Model as for the experimental RFs.
We recovered spatial RF structures from 94 well isolated single-units in
3 cats, of which 26 were classified as simple cells, 38 as complex cells
and 30 as a mixed cell class.
The results confirmed the qualitatively similarity of theoretical RF structures from Efficient Coding with those estimated experimentally. However, quantitatively a number of discrepancies were observed as well
as similarities. (1) RF orientation tuning was wider experimentally than
theoretically (bandwidth was most frequently between 60° and 90°
experimentally, while theoretically, it was mostly between 30° and 60°).
(2) Spatial frequency tuning was wider experimentally than theoretically (bandwidth was most frequently 2 ± 0.5 octaves experimentally,
but only 1 ± 0.5 octaves theoretically). (3) For cells with more than one
sub-RF it was possible to compare the tuning to orientation and spatial frequency between different sub-RFs. The difference in orientation
tuning between sub-RFs showed that experimentally around 60% cells
had precisely matched orientation preferences (<15°), while in the theoretical population this proportion dropped to around 40%. (4) Experimentally, the spatial frequency preference of sub-RFs in the same cell
were also tightly matched for the majority of cells (<0.5 octaves), with a
similar result in the theoretical population (<0.5 octaves). (5) Finally, the
spatial phase relationships of sub-RFs were compared: experimentally a
large majority (80%) of cells that had two quadratic sub-RFs that were
90° ± 15° out of phase. In the theoretical population, this spatial phase
relationship was common but less prevalent (50%).
The quantitative discrepancies we found were robust to changes in
meta-parameters, such as the degree of image compression in preprocessing or the source of natural images. The results suggest that
the experimental RFs are sub-optimal in terms of coding efficiency.
However, it is important to note that we used a deterministic model of
spike rate in response to an image stimulus: a stochastic model is more
realistic and may limit the coding efficiency of the theoretical result,
bringing it in closer quantitative agreement with experiment.
Acknowledgements
AA acknowledges a Melbourne University Postgraduate Research
Award. HM and MI acknowledge support from the Australian Research
Council Centre of Excellence for Integrative Brain function.
P175
Cholinergic Modulation of DG‑CA3microcircuit dynamics
and function
Luke Y. Prince1, Krasimira Tsaneva-Atanasova2,3, Jack R. Mellor1
1
Centre for Synaptic Plasticity, School of Physiology, Pharmacology,
and Neuroscience, University of Bristol, Bristol, BS8 1TD, UK; 2Department
of Mathematic, College of Engineering, Mathematics and Physical
Sciences, University of Exeter, Exeter, UK, EX4 4QF; 3EPRSC Centre
for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK, EX4
4QJ
Correspondence: Luke Y. Prince (l.y.prince@bristol.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P175
BMC Neurosci 2017, 18(Suppl 1):60
Dentate gyrus granule cells provide powerful feedforward excitatory drive onto a local circuit of CA3 pyramidal cells and inhibitory interneurons, and is believed to selectively activate subsets of
pyramidal cells in the CA3 recurrent network for encoding and recall
of memories. Cholinergic receptors provide a key means to modulate
this circuit, increasing cellular excitability and altering synaptic release,
but the combined action of these changes on information processing
between the dentate gyrus and CA3 remains unknown. We recorded
evoked monosynaptic EPSCs and disynaptic IPSCs in CA3 pyramidal
cells in response to a range of frequencies and stimulation patterns
and in the presence and absence of the cholinergic receptor agonist
carbachol (5 μM). We found that carbachol strongly reduced IPSC
amplitudes but only mildly reduced EPSC amplitudes. The short-term
plasticity dynamics of these responses were used to constrain a computational model of mossy fibre driven transmission across a range
of stimulation patterns. This model was then used to analyse how
aceytlcholine influences encoding and recall in a spiking neural network model of CA3 to study encoding and recall of neuronal ensembles driven by mossy fibre input. We found that acetylcholine lowers
the requirements for encoding neuronal ensembles and increases
memory storage in CA3.
P176
Subthalamic nucleus low frequency fluctuations carry information
about future economic decisions in parkinsonian gamblers
Alberto Mazzoni1†, Manuela Rosa2†, Jacopo Carpaneto1, Luigi M. Romito3,
Alberto Priori2,4, Silvestro Micera1,5
1
Translational Neural Engineering, The Biorobotics Institute, Scuola
Superiore Sant’Anna, Pontedera, 56025, Italy; 2Clinical Center
for Neurostimulation, Neurotechnology and Movement Disorders
Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan,
20122, Italy; 3Movement Disorders Department, Neurological Institute
Carlo Besta, Milan, 20133, Italy; 4Department of Health Sciences,
University of Milan & ASST Santi Paolo e Carlo, Milan, 20142, Italy;
5
Bertarelli Foundation Chair in Translational NeuroEngineering, Institute
of Bioengineering and Center for Neuroprosthetics, Ecole Polytechnique
Federale De Lausanne, Lausanne, CH-1015, Switzerland
Correspondence: Alberto Mazzoni (alberto.mazzoni@santannapisa.it)
†
equal first author contribution
BMC Neuroscience 2017, 18 (Suppl 1):P176
Dopamine replacement therapy for the treatment for Parkinson
Disease (PD) has been related to an increased risk of occurrence of
Impulse Control Disorders (ICD), such as Gambling Disorder (GD) [1].
Previous experimental and modeling studies [2] have shown a link
between ICD and specific activity of the subthalamic nucleus (STN), a
standard target for Deep Brain Stimulation (DBS) therapy for advanced
PD. Several brain areas involved in decision making, impulsivity and
reward valuation, such as the prefrontal cortex and striatum, are interconnected to the STN, and activity in these areas might be modulated
by STN DBS. Understanding the relationship between STN functioning
and ICD would help developing better therapies for PD while shedding light on the mechanisms of human decision making.
To study how STN activity is modulated by gambling, we analyzed
low-frequency ([1–12] Hz) fluctuations of STN LFP recorded by DBS
electrodes from PD patients during an economic decision making
task. All patients were under dopamine replacement therapy, and
half of them were affected by GD. In the task patients were asked to
decide between a high risk (HR) and low risk (LR) option, the first being
associated to a negative expected value, but to a high reward in case
of win. Reaction times were strongly affected by trial type, with GD
patients and non-GD patients quicker in taking HR and LR decisions
respectively, suggesting that decision is actually determined before
options presentation. Analyzing low frequency STN LFP we found that
amplitude of fluctuations, recorded during specific intervals preceding option presentation, carried significant information about future
choices on single trials in patients affected by GD but not in those not
affected.
Page 105 of 176
These results complement previous studies about the role of inhibiting impulsive behavior displayed by the STN activity. Beta-range STN
fluctuations were found to be modulated by the level of conflict in
decisions [3], while our results suggest that the lower frequencies,
which are functionally correlated with different cortical areas [4], play
instead a role to prevent pathological risk attraction.
Acknowledgements
This work was supported by institutional funds from Scuola Superiore
Sant’Anna, by the Italian Ministry of Health (GR-2009-1594645 grant),
by the Aldo Ravelli Donation for Research on Parkinson Disease, by the
Bertarelli Foundation, and by institutional funds from École Polytechnique Federale de Lausanne.
References
1. Weintraub D, David AS, Evans AH, Grant JE, Stacy M: Clinical spectrum of
impulse control disorders in Parkinson’s disease. Mov. Disord. 2015 30:
121–127.
2. Frank MJ, Samanta J, Moustafa AA, Sherman SJ: Hold Your Horses: Impulsivity, Deep Brain Stimulation, and Medication in Parkinsonism. Science 2007
318: 1309–1312.
3. Brittain JS, Watkins KE, Joundi RA, Ray NJ, Holland P, Green AL, Aziz TZ, Jenkinson N A Role for the Subthalamic Nucleus in Response Inhibition during
Conflict. J. Neurosci. 2012 32: 13396–13401.
4. Herz DM, Tan H, Brittain JS, Fischer P, Cheeran B, Green AL, FitzGerald J, Aziz
TZ, Ashkan K, Little S, et al. Distinct mechanisms mediate speed-accuracy
adjustments in cortico-subthalamic networks. eLife 2017 6: 10.7554/
eLife.21481
P177
Data‑driven computational modeling of CA1
hippocampal principal cells and interneurons
Rosanna Migliore1, Carmen Alina Lupascu1, Francesco Franchina1, Luca
Leonardo Bologna1, Armando Romani2, Christian Rössert2, Sára Saray3,
Jean-Denis Courcol2, Werner Van Geit2, Szabolcs Káli3, Alex Thomson4,
Audrey Mercer4, Sigrun Lange4,5, Joanne Falck4, Eilif Muller2, Felix
Schürmann2, and Michele Migliore1
1
Institute of Biophysics, National Research Council, Palermo, Italy;
2
Blue Brain Project, École Polytechnique Fédérale de Lausanne Biotech
Campus, Geneva, Switzerland; 3Institute of Experimental Medicine,
Hungarian Academy of Sciences, Budapest, Hungary; 4University College
London, London, United Kingdom; 5University of Westminster, London,
United Kingdom
Correspondence: Rosanna Migliore (rosanna.migliore@cnr.it)
BMC Neuroscience 2017, 18 (Suppl 1):P177
We present and discuss data-driven models of biophysically detailed
hippocampal CA1 pyramidal cells and interneurons of a rat. The results
have been obtained by using the Brain Simulation Platform (BSP) of
the Human Brain Project and two open-source packages, the Electrophys Feature Extraction Library (eFEL, https://github.com/BlueBrain/
eFEL) and the Blue Brain Python Optimization Library (BluePyOpt)
[1]. They have been integrated into the BSP in an intuitive graphical user interface guiding the user through all steps, from selecting
experimental data to constrain the model, to run the optimization
generating a model template and, finally, to explore the model with
in silico experiments. Electrophysiological features were extracted
from somatic traces obtained from intracellular paired recordings
performed using sharp electrodes on CA1 principal cells and interneurons with classical accommodating (cAC), bursting accommodating
(bAC) and classical non-accommodating (cNAC) firing patterns. The
extracted features, together with user selections for realistic morphological reconstructions and ion channel kinetics, were then used to
automatically configure and run the BluePyOpt on the Neuroscience
Gateway and/or on one of the HPC systems supporting the BSP operations, such as CINECA (Bologna, Italy) and JSC (Jülich, Germany) in this
case. The resulting optimized ensembles of peak conductances for the
ionic currents, were used to explore and validate the model behavior
during interactive in silico experiments carried out within the HBP
BMC Neurosci 2017, 18(Suppl 1):60
Page 106 of 176
Collaboratory. Such a modelling effort has been undertaken in the
context of the Human Brain Project and constitutes one of the major
steps in the workflow that is being used to build a cellular level model
of a rodent hippocampus.
Acknowledgements
This project has received funding from the European Union’s Horizon
2020 research and innovation programme under grant agreement No
720270
Reference
1. Van Geit W, Gevaert M, Chindemi G, Rössert C, Courcol J-D, Muller EB,
Schürmann F, Segev I and Markram H (2016) BluePyOpt: Leveraging Open
Source Software and Cloud Infrastructure to Optimise Model Parameters in
Neuroscience. Front. Neuroinform. 10:17. doi: 10.3389/fninf.2016.00017
P178
The interplay between basal ganglia and cerebellum in motor
adaptation
Dmitrii Todorov, Robert Capps, William Barnett, Yaroslav Molkov
Department of Mathematics and Statistics, Georgia State University,
Atlanta, Georgia 30303-3083, USA
Correspondence: Dmitrii Todorov (dtodorov@gsu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P178
It is widely accepted that the cerebellum and basal ganglia (BG) and
play key roles in motor adaptation (in error based and non-error based
one, respectively) [1]. However, despite considerable number of studies, the interactions between BG and cerebellum are not completely
understood [1]. In particular, in the experiments it is difficult to dissociate the adaptation performed by cerebellum and by BG. To do so,
some studies [2] introduced perception perturbations that were suggested to impair cerebellum’s ability to adapt to errors, and, thus, promoted the BG-based mechanisms. To our knowledge no mathematical
model exists that explains the conditions in which visual perturbations
make reinforcement learning in the BG the main mechanism of motor
adaptation.
We have developed a model that integrates a phenomenological
representation of the cerebellum and a previously published firing
rate-based description of BG network [3], and mimics the trial-totrial motor adaptation in 2D reaching arm movements. Cerebellum is
implemented as an artificial neural network performing corrections of
the motor program, descending from motor cortex to spinal cord, via
supervised learning.
Figure 1 below shows the model architecture. Stimulus signal comes
from prefrontal cortex (PFC) and is sent to direct and indirect pathways of BG. The strength of PFC → BG connections changes due to
reinforcement learning mediated by substantia nigra pars compacta
(SNc) dopaminergic input, whose activity is defined by the reward
prediction error (RPE) signal. Direct and indirect pathways converge
at globus pallidus internus (GPi)/substantia nigra pars reticulata (SNr),
which together project to premotor cortex (PMC)/Thalamus to perform action selection. There are also direct PFC → PMC connections
representing habitual cue-action associations. The PMC/Thalamus
then project to the motor cortex (MC) and to the cerebellum. Cerebellum output represents a correction, which adds to the motor command descending from the MC to the spinal cord. This correction
is calculated as a linear transformation of the motor command. The
transformation matrix is updated by the supervised learning algorithm, accounting for the vector error provided by the visual feedback.
The corrected signal goes to the spinal cord neuron network that controls a two-joint arm to perform center-out reaching movements. The
perceived movement endpoint of the is used to compute the vector
error and/or the reward.
Figure 1. Model architecture
Our model simulations suggest that when the perception of the vector
error provided to the cerebellum is significantly perturbed, the faulty
cerebellar corrections adversely affect or even completely destroy
motor adaptation. We speculate and show via simulations that errorbased learning in cerebellum has an adaptive critic component which
effectively suppresses error-based mechanisms to enable reinforcement-based motor adaptation.
References
1. Izawa J, Shadmehr R. Learning from sensory and reward prediction errors
during motor adaptation. PLoS Comput Biol. 2011; 7(3):e1002012.
2. Gutierrez‐Garralda JM, Moreno‐Briseño P, Boll MC, Morgado‐Valle C, Campos‐Romo A, Diaz R, Fernandez‐Ruiz J. The effect of Parkinson’s disease and
Huntington’s disease on human visuomotor learning. European Journal of
Neuroscience 2013;38(6):2933–40.4.
3. Kim T, Hamade KC, Todorov D, Barnett WH, Capps RA, Latash EM, Markin SN,
Rybak IA, Molkov YI. Reward based motor adaptation mediated by basal
ganglia. Frontiers in Computational Neuroscience. 2017;11.
P179
Microscopic and macroscopic dynamics of neural populations
with delays
Federico Devalle1,2, Diego Pazó3, Ernest Montbrió1
1
Center for Brain and Cognition, Universitat Pompeu Fabra, 08018
Barcelona, Spain; 2Department of Physics, Lancaster University, LA1 4YB
Lancaster, UK; 3Instituto de Fisica de Cantabria (IFCA), CSIC-Universidad
de Cantabria, 39005 Santander, Spain
Correspondence: Federico Devalle (federico.devalle@upf.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P179
Bridging descriptions of brain activity across different scales is a
major challenge for theoretical neuroscience. Numerous experimental techniques are available to measure brain activity, ranging from
single cells recordings to population measurements of the average
activity of large ensembles of neurons. It is often in these populationlevel recordings (e.g. EEG, MEG…), that important phenomena are
observed. A particularly relevant example are gamma oscillations, a
temporal coherent activity with frequency between 30 and 100 Hz.
A large body of experimental and computational works indicates
that the interplay between synaptic processing and recurrent inhibition is the key ingredient to generate such oscillations, in a mechanism commonly referred to as Interneuronal Gamma oscillations
(ING) [1, 2]. Here, we analyse the dynamics of a network of quadratic
BMC Neurosci 2017, 18(Suppl 1):60
Page 107 of 176
integrate-and-fire neurons with time-delayed synaptic interactions,
both in their excitable and self-oscillatory regime. Time delays have
been indeed shown to approximate the effect of synaptic kinetics [3]. Using the so-called Lorentzian ansatz [4, 5], we derive a set of
two delayed firing rate equations (FREs). Due to their analytical tractability, the FREs allow us to find exact boundaries of stability for the
parameters regions of oscillatory (collective synchrony-CS) and asynchronous dynamics. Moreover, for inhibitory coupling, we observe a
more complex oscillatory state, the so-called quasiperiodic partially
synchronized state (QPS). Here, neurons are quasiperiodic, and have a
mean frequency different from the global frequency of the entire population, which corresponds to fast brain oscillations (f ~ 80 Hz). Interestingly, macroscopically this state strongly resembles the sparsely
synchronized state observed in networks of leaky integrate-and-fire
neurons subjected to strong recurrent inhibition and noise [6]. However, microscopically, these two states have qualitatively different
dynamics, suggesting a dichotomy between microscopic and macroscopic dynamics. For a certain region of parameters, the QPS coexists
also with the CS. Moreover, sufficiently increasing inhibition, the QPS
undergoes a series of period doubling bifurcation that eventually
leads to chaos. Notably, only the collective dynamics is chaotic, while
microscopically neurons are non-chaotic. Finally, we find that while
excitation always leads to collective synchronous oscillations, inhibition fails to synchronize neural activity when a precise degree of heterogeneity is exceeded, consistently with previous numerical studies
of heterogeneous, inhibitory spiking neural networks [7].
the number of trial remaining to the reward [2]. Concurrently, the firing rate of ACC neurons gradually increased or decreased along with
reward expectancy. It happened when the reward was certain and correct decisions could only bring animal closer to the reward. However,
when certainty about outcome was removed and no notion of reward
proximity was provided the progressive modulation of behavior and
ACC activity disappeared.
Here we tested whether such motivation signal can be also found in
the circumstances when the reward is no longer certain and the animal
choices brings reward closer or further away but the information about
reward closeness reminds - the situation more common in the economic
decisions of everyday life. We recorded single unit activity from dorsal
ACC while monkey performed token gambling task. On each trial, monkeys gambled to gain certain number of tokens, but they could also lose
tokens. The collection of six tokens resulted in a jackpot reward delivery. The number of collected tokens was displayed on the monitor and
was known to the animal. The animal learnt the task and exhibited risk
seeking behavior as previously reported [3]. The analysis of behavioral
data revealed that animal performance (percent of correct responses)
depended on the number of previously collected tokens. The relation
was not monotonic with the drop of performance after reward administration. At the same time, the significant fraction of recorded neurons
exhibited tuning towards the number of previously collected tokens.
Our preliminary results suggest that ACC monitors rewards in risky
conditions, and that neuronal signals could be directly related to the
motivation of the animal.
Acknowledgements
We acknowledge support by MINECO (Spain) under project No.
~FIS2014-59462-P, and the project COSMOS of the European Union’s
Horizon 2020 research and innovation programme under the Marie
Sklodowska-Curie grant agreement No.642563.
Acknowledgements
The Spanish Ministry of Economy and Competitiveness IJCI-201421937 grant (to G. M.); the Marie Curie FP7-PEOPLE-2010-IRG grant
PIRG08-GA - 2010-276795, and the Spanish Ministry of Economy and
Competitiveness PSI2013-44811-P grant (to R. M. B.)
References
References
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View. Annu Rev Neurosci 2016, 39: 149–170.
2. Shidara M, Richmond BJ: Anterior Cingulate: Single Neuronal
Signals Related to Degree of Reward Expectancy. Science 2002,
296(5573):1483–1490.
3. Azab H, Hayden BY: Shared roles of dorsal and subgenual anterior cingulate cortices in economic decisions. bioRxiv 2016. [http://biorxiv.org/
content/early/2016/09/09/074484].
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3. Roxin A, Montbrió E: How effective delays shape oscillatory dynamics in
neuronal networks. Physica D 2011, 240: 323–345.
4. Montbrió E, Pazó D, Roxin A: Macroscopic description for Networks of Spiking Neurons. Phys Rev X 2015, 5: 021028
5. Pazó D, Montbrió E: From Quasiperiodic Partial Synchronization to Collective Chaos in Populations of Inhibitory Neurons with Delay. Phys Rev Lett
2016, 116: 238101
6. Brunel N, Hakim V: Fast global oscillations in networks of integrate-and-fire
neurons with low firing rates. Neural Comput 1999, 11:1621.
7. Wang XJ, Buzsáki G: Gamma Oscillations by Synaptic inhibition in
a Hippocampal Interneuronal Network Model. J Neurosci 1996,
16(20):6402–6413.
P180
Motivation signal in anterior cingulate cortex during economic
decisions
Gabriela Mochol1, Habiba Azab2, Benjamin Y. Hayden2, Rubén
Moreno-Bote1
1
Center for Brain and Cognition and Department of Information
and Communications Technologies, University Pompeu Fabra, Barcelona,
08005, Spain; 2Department of Brain and Cognitive Sciences and Center
for Visual Sciences, University of Rochester, Rochester, NY 14618, USA
Correspondence: Gabriela Mochol (gabriela.mochol@upf.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P180
Anterior cingulate cortex (ACC) plays regulatory and cognitive roles.
Its functions are associated with conflict and performance monitoring, regulation of strategy and response selection, all of which depend
on reward monitoring and its anticipation [1]. It has been shown
previously that in the condition when the reward was certain and
its proximity was cued, animal’s error rate decreases together with
P181
A simple computational model of altered neuromodulation
in cortico‑basal ganglia dynamics underlying bipolar disorder
Pragathi Priyadharsini Balasubramani1, Srinivasa V. Chakravarthy2,
Vignayanandam R. Muddapu2
1
Brain and Cognitive Sciences, University of Rochester, Rochester,
New York 14627, USA; 2Bhupat and Jyoti Mehta School of Biosciences,
Department of Biotechnology, IIT- Madras, Chennai, TN, India
Correspondence: Srinivasa V. Chakravarthy (schakra@iitm.ac.in)
BMC Neuroscience 2017, 18 (Suppl 1):P181
Bipolar disorder (BPD) is characterized by oscillations alternating
between manic and depressive episodes causing swings in moods.
The length of an episode in a patient’s mood cycle (time period) can
vary from hours to years. Some medications popularly used for stabilizing mood include selective serotonin reuptake inhibitors and lithium
therapy. This computational study focuses on the serotonergic system
dysfunction, and particularly, understanding their contribution to cortico-basal ganglia network (CGBN) dynamics for stability and recurrence
of moods. To this end, we try to model the disorder in a decision-making
framework that tries to choose between actions of positive or negative
affects. We propose a computational model that explores the effects of
impaired serotonergic neuromodulation on the dynamics of CBGN and
relate this impairment to the manic and depressive episodes of BPD. The
proposed model of BPD is derived from an earlier model, that describes
the roles of dopamine and serotonin in the action selection dynamics of
CBGN. In that model, rewarding actions are selected based on the Utility
function, which combines Value and Risk functions as follows (eqn. 1).
BMC Neurosci 2017, 18(Suppl 1):60
Ut (st , at ) = Qt (st , at ) − α sign(Qt (st , at ))
Page 108 of 176
ht (st , at ) (1)
where U, Q and h represent Utility, Value and Risk respectively, for a
given state, s, and action, a, at time, t. The parameter α, which represents risk preference, is associated with serotonin action in CBGN.
Value and Risk are trained by Reinforcement Learning using the Temporal Difference (TD) error, which represents dopamine in CBGN. The
lumped model was later extended to a detailed network model of BG.
In those models, α was a constant, whereas in the current model it varies as per the following dynamics:
α̇ = τα (−α + Ar r̄ + αk )
(2)
r̄˙ = τr (r − r̄)
(3)
The variable r-bar tracks the average rewards ‘r’ gained through time,
and α-dot defines serotonin dynamics with αk constant (eqns. 2, 3)
indicating basal risk sensitivity levels. The parameter Ar denotes the
amplitude of reward sensitivity, and thus the reward history is proposed to modulate α dynamics. When the model is run in a simple two
arm bandit task - one rewarding (+ve reward) and the other punitive
(-ve reward) with probability 0.5, under normal conditions the network
shows high preference for rewarding actions. But for certain ranges of
reward sensitivity (Ar) and basal risk sensitivity (αk) the model exhibits oscillations reminiscent of BPD mood oscillations (Fig. 1). There
exists clinical and experimental evidence supporting abnormality
in serotonin levels and reward sensitivity in case of BPD. Specifically,
high reward sensitivity with medium levels of risk sensitivity (serotonin activity correlate, as tonic/basal levels or that induced by medication), can trigger bipolar mood oscillations. This preliminary model can
be extended to a detailed network model. Future work will include
expanding CBGN with neural models of limbic system, and predicting
plausible treatment strategies for effectively dealing with the onset
and progression of BPD symptoms.
Figure 1. Action (positive or negative affect) selection in CBGN
model: Yellow: rewarding (+ve) action selection as in healthy controls; Green: Oscillations between +ve and –ve actions as in BPD;
Blue: -ve action selection as in depression
P182
Theta/alpha coordination of pre‑motor and parietal networks
during free behavior in rats
Medorian D. Gheorghiu1, Bartul Mimica2, Jonathan Withlock2, Raul C. Mureșan1
1
Romanian Institute of Science and Technology, Cluj-Napoca, Cluj
400552, Romania; 2Centre for Neural Computation, Kavli Institute
for Systems Neuroscience, Trondheim, NO-7491, Norway
Correspondence: Medorian D. Gheorghiu (medorian@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P182
Activity of posterior parietal cortex (PPC) neurons exhibits self-motion
tuning to both ongoing and impeding movements, which may reflect
behavioral planning [1]. A major input to PPC originates from the frontal
medial agranular cortex (AGm), which is believed to be involved in complex motor planning. In the monkey, Pesaran and colleagues [2] showed
that fronto-parietal coherence is stronger in free-choice tasks than in
instructed trials, probably activating different decision-related circuits
in these areas. Therefore, we hypothesize that in the rat the interaction
between AGm and PPC may be instrumental in coordinating decision
making and motor planning. Here, we are investigating the coupling
strength between PPC and AGm in the theta/alpha frequency band by
computing pairwise spectral coherence and phase delays across the two
areas (see Figure 1) during goal-directed spatial navigation in rats. Two
tasks were implemented: an instructed or “known” task where the rat had
to run straight to a fixed well named “Home”; an “exploratory” task where
the rat had to search for reward delivered in “Target” wells located randomly across the arena and then run back to the Home well.
Results: As the rat stopped running and started licking at the target
well, there was an increase in theta coupling strength accompanied
by a gradual decrease in frequency (Figure 1A). Using the phase information, we computed the delay of PPC relative to AGm. The delay
decreased sharply from ~5.5 to ~2.5 ms when the rat arrived at the target location (see Figure 1B), and it was gradually resetting in the last
5 s that the rat spent at that location (see Figure 1D). As suggested by
anatomical evidence, AGm was leading PPC indicating a causal interaction where AGm coordinates the activity in PPC.
Conclusions: Our results indicate a complex regulation of oscillatory
behavior in PPC and AGm during free behavior in rats. In particular, a
pronounced ongoing oscillation in the theta/alpha band is expressed
throughout the task and seems to be coordinated across the two areas.
AGm leads PPC and both the frequency of the oscillation and the time
delay between the two areas change as a function of behavioral events.
Figure 1. A and C. Time-resolved spectral coherence between PPC
and AGm in the 6-10 Hz frequency band, aligned to the initiation
(A) and cessation (C) of licking at the target well. B and D. phase
delays in ms between PPC and AGm aligned to the initiation (B)
and cessation (D) of licking at the target well
Acknowledgements
This work was supported by CNCS - UEFISCDI (PN-II-RU-TE-2014-4-0406
and PN-III-P3-3.6-H2020-2016-0012).
References
1. Withlock J., Robert J. Sutherland, Menno P. Witter, May-Britt Moser, Edvard
I. Moser: Navigating from hippocampus to parietal cortex. PNAS 2008 vol.
105.39: 14755–14762;
BMC Neurosci 2017, 18(Suppl 1):60
Page 109 of 176
2. Pesaran B, Nelson MJ, Andersen RA: Free choice activates a decision circuit
between frontal and parietal cortex. Nature 2008: 406–409.
notion that alterations in precise spike timing may be an underlying
driving factor towards reduced functional connectivity in schizophrenia,
providing a new mechanistic model for disease pathophysiology.
P183
Information theoretic approach towards identifying changes
in cellular‑level functional connectivity and synchrony
across animal models of schizophrenia
Jennifer L. Zick1,2, Kelsey Schultz4, Rachael K. Blackman1,2,3, Matthew V.
Chafee1,3, Theoden I. Netoff1,4
1
Graduate Program in Neuroscience, University of Minnesota, Minneapolis,
MN 55455 USA; 2Medical Scientist Training Program (MD/PhD), University
of Minnesota, Minneapolis, MN 55455 USA; 3Brain Sciences Center, VA
Medical Center, Minneapolis, MN 55417 USA; 4Department of Biomedical
Engineering, University of Minnesota, Minneapolis, MN 55455 USA
Correspondence: Jennifer L. Zick (zick@umn.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P183
Acknowledgements
This material is based upon work supported by the NIH (R01
MH1107491; Chafee); NRSA F30 MH108205-01A1 (Zick); NSF Career
Award (TIN); Medical Scientist Training Program NIH T32-008244
Schizophrenia has long been described as a syndrome of disordered
connectivity in the brain. While originally based on clinical symptomatology, neurophysiological evidence for this concept has been found
in imaging studies in humans with schizophrenia. It has also been
found that cortical pyramidal neurons have a reduced density of the
synaptic spines necessary for cellular communication in postmortem
brain tissue recovered from people with schizophrenia. However,
functional evidence for disconnectivity at the level of local neuronal
circuits is limited. To address this question, we characterized neuronal
dynamics between groups of simultaneously recorded cortical neurons in data obtained from both primate and mouse models of schizophrenia. Neural data were obtained from multielectrode recording
arrays inserted into the parietal and prefrontal cortices of macaque
monkeys while the animals performed a cognitive control task that
measures a specific cognitive impairment in human patients with
schizophrenia. Phencyclidine, an NMDA receptor (NMDAR) antagonist
that has long been used as a pharmacological model of psychosis, was
administered systemically on alternating days with injections of saline.
In the mouse experiments, analogous data were obtained from medial
prefrontal cortex in awake head-fixed mice during locomotion. Data
from Nestin-promoted Dgcr8+/− mutant mice (DiGeorge syndrome critical region 8; a gene strongly associated with schizophrenia in humans
and shown to produce schizophrenia-like symptomatology in mice) is
compared with that obtained from wildtype littermate controls.
Cross-correlation analysis was performed on spike trains from pairs of
simultaneously recorded neurons to characterize changes in synchrony
between conditions. In the primate neural data, cross correlations frequently displayed a prominent “zero-lag” peak representing a large number of coincident action potentials between cells in the control condition
that could be a result of common input. In the phencyclidine condition,
there was a reduction in synchronous firing between pairs of cells. A similar
rate-independent reduction in precise synchrony was also found in medial
prefrontal cortical neuronal ensemble recordings obtained from Dgcr8
mice as compared to controls, suggesting that this is may be a consistent
finding related to the root pathophysiology of schizophrenic processes.
To characterize deficits in synaptic communication between neurons
in the disease state, we employed higher-order transfer entropy (TE)
metrics to identify pairs of cells that exhibited effective connectivity
(Ito et al, 2011, PLOS One). Consistent with the disconnection hypothesis of schizophrenia, we found that acute administration of PCP
resulted in a reduction in the percent of cell pairs identified as significantly functionally connected by TE analysis, as well as a reduction in
the overall distribution of population shared information. This result
suggests a cellular basis for the reduced information-processing capabilities seen in schizophrenics performing prefrontal cortex-dependent tasks, as well as synaptic disconnection. Furthermore, this result is
supported by a similar reduction in both number of functionally connected cell pairs and overall shared information in prefrontal cortex in
the Dgcr8+/− mouse genetic model of schizophrenia.
In summary, these results display a reduction in both zero-lag synchrony
and cellular-level functional connectivity in two very distinct animal
models of schizophrenia. It is well known that coincident firing of action
potentials facilitates connectivity between neurons, and asynchrony
results in disconnection. Thus, the results presented here support the
P184
Neural Suppression with Deep Brain Stimulation using a Linear
Quadratic Regulator
Nicholas Roberts1, Vivek Nagaraj2,, Andrew Lamperski3, Theoden I. Netoff1
1
Department of Biomedical Engineering, University of Minnesota,
Minneapolis, MN 55455, USA; 2Graduate Program in Neuroscience,
University of Minnesota, Minneapolis, MN 55455, USA; 3Department
of Electrical and Computer Engineering, University of Minnesota,
Minneapolis, MN 55455, USA
Correspondence: Nicholas Roberts (robe1521@umn.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P184
Current neuromodulation techniques for seizure suppression, such as
vagus nerve or deep brain stimulation, have shown some clinical efficacy. Yet their application is complicated by the large parameter space
of electrical stimulation settings inherent to these systems. A physician
must skillfully choose stimulation parameters such as frequency, amplitude, and pulse width for each individual patient in order to effectively
reduce their incidence of seizures. We demonstrate an algorithm capable of automatically generating a continuous stimulation waveform to
suppress neural activity and minimize total stimulation energy.
We treat the suppression of neural activity as a linear-quadratic-Gaussian (LQG) control problem. The resulting optimal controller consists
of a Kalman filter and a linear-quadratic regulator (LQR). The effectiveness of the LQG controller in suppressing seizure biomarkers was
first verified in a computational model of epilepsy called Epileptor [1],
which simulates local field potential (LFP) recordings within a seizure
focus. We built a model of the generated LFPs using the Ho-Kalman
algorithm [2] for subspace system identification. The Kalman filter estimated the state of the system and a feedback control signal provided
by the LQR successfully prevented seizures during stimulation, even
while varying the Epileptor model parameters.
We then implemented the LQG controller in an in vivo rodent model.
We stimulated the ventral hippocampal commissure while recording
in the hippocampus. The Ho-Kalman algorithm was again used to build
a dynamical systems model of the LFP activity based on the evoked
response to Gaussian white noise stimulation. We used a three-phase
experiment to test the LQG controller: 2 min of baseline activity; 2 min
of closed-loop neural stimulation; and 2 min post-stimulation to check
if LFPs return to baseline levels. This same stimulation waveform was
then replayed in “open-loop,” without state estimation from the Kalman
filter. The LFP power from 1-100 Hz was used to measure performance.
Our results show a significant decrease in LFP power during closed-loop
stimulation. Open-loop stimulation produced negligible change in LFP
power. The LQG controller was confirmed to be an effective tool for minimizing LFP activity within a selected frequency band. The mathematical models of neural dynamics it uses are subject specific and determine
stimulation waveforms based on state to suppress neural activity.
References
1. Jirsa VK, Stacey WC, Quilichini PP, Ivanov AI, Bernard C: On the nature of
seizure dynamics. Brain 2014, 137 (pt. 8):2210–2230.
2. Miller DN, & de Callafon RA: Identification of linear time-invariant systems
via constrained step-based realization. IFAC Proceedings Volumes 2012,
45(16): 1155–1160.
P185
Reinforcement learning for phasic disruption of pathological
oscillations in a computational model of Parkinson’s disease
Logan L. Grado1, Matthew D. Johnson1,2, Theoden I. Netoff1
1
Department of Biomedical Engineering, University of Minnesota,
Minneapolis, MN, 55455, United States; 2Institute for Translational
BMC Neurosci 2017, 18(Suppl 1):60
Neuroscience, University of Minnesota, Minneapolis, MN, 55455, United
States
Correspondence: Logan L. Grado (grado@umn.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P185
Deep brain stimulation (DBS) is an effective therapy for motor symptoms
of PD, and is often used as a complement to medication in patients who
have progressed to severe stages of PD. However, programming these
devices is difficult and time consuming, and DBS therapy is limited by
side effects and partial efficacy [1]. Furthermore, traditional continuous
DBS (cDBS) does not account for fluctuations in motor symptoms caused
by factors such as sleep, attention, stress, cognitive and motor load, and
current drug therapy [2], and as the patient’s state changes, so does the
need for stimulation. Current cDBS strategies are incapable of adapting
to the needs of patients: once the clinician sets the parameters, they do
not change until the next programming visit. In this study, we have created a reinforcement learning (RL) algorithm capable of learning online
how best to stimulate to reduce pathological oscillations in silico. We have
developed the reinforcement learning DBS (RL-DBS) algorithm for tuning
DBS parameters, and have tested it on a biophysically realistic mean-field
model of the basal ganglia-thalamocortical system (BGTCs) [3], simulating parkinsonian neural activity. The RL-DBS algorithm decides when to
deliver stimulus pulses based upon the real-time amplitude and phase
of the pathological oscillation in order to reduce the amplitude of that
oscillation. The algorithm learns which actions lead to the highest cumulative reward (i.e. reduction of oscillation amplitude). After training on the
model, the RL-DBS algorithm is able to learn both phase and amplitude
selectivity to optimally reduce the pathological oscillation. The algorithm
learns the expected reward for both actions (not stimulating and stimulating) as a function of the phase/amplitude of the oscillation (Figure. 1A,
Figure. 1B). The algorithm then decides which action to execute based
upon the action difference (Figure. 1C). Additionally, the algorithm learns
to deliver bursts of stimulation phase-locked to the oscillation.
We created an adaptive RL-DBS algorithm capable of learning on-line
how to reduce the power of a pathological oscillation in a computation
model of PD. The algorithm has the potential to deliver individualized,
adaptive DBS therapy that can improve the quality of life for PD patients.
Figure. 1. Learned reward maps A, B and action difference C as
a function of the phase and amplitude of the oscillation. A and
B show the learned reward for no stimulation and stimulation
respectively, while C shows the action difference. The algorithm
selects the action that with the highest expected reward. The
action difference reveals that the algorithm learns both phase- and
amplitude-selective stimulation
Page 110 of 176
Acknowledgements
Research supported by the Systems Neuroengineering NSF IGERT Program (DGE-1069104), NIH R01-NS094206, NIH P50-NS098573, and NSF
CBET-1264432.
References
1. G. Deuschl, S. Paschen, and K. Witt: Clinical outcome of deep brain stimulation for Parkinson’s disease. Handb. Clin. Neurol., vol. 116, pp. 107–128, 2013.
2. J. a Obeso, M. C. Rodríguez-Oroz, M. Rodríguez, J. L. Lanciego, J. Artieda,
N. Gonzalo, and C. W. Olanow: Pathophysiology of the basal ganglia in
Parkinson’s disease. Trends Neurosci., vol. 23, no. 10 Suppl, pp. S8–S19, 2000.
3. S. J. van Albada and P. a Robinson. Mean-field modeling of the basal
ganglia-thalamocortical system. I Firing rates in healthy and parkinsonian
states. J. Theor. Biol., vol. 257, no. 4, pp. 642–63, Apr. 2009.
P186
Metrics for detection of delayed and directed coupling
David P. Darrow1, Theoden I. Netoff2
1
Department of Neurosurgery, University of Minnesota, Minneapolis,
MN 55455, USA; 2Department of Biomedical Engineering, University
of Minnesota, Minneapolis, MN 55455, USA
Correspondence: David P. Darrow (Darro015@umn.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P186
Detecting delayed coupling in dynamical systems remains a challenging
frontier in Neuroscience. Frequently used tools such as cross-correlation
have been shown to be robust against measurement noise but fail to
identify coupling direction. [1] More recently developed tools such as
multivariate granger causality and various forms of transfer entropy provide methods of detecting direction of coupling but may be less resilient
to measurement noise and require more substantial quantities of data
depending on the signal to noise ratio. With widespread use of these tools,
it is important to have a complete understanding of the limitations of each
metric and the circumstances of optimal use in experimental design.
To test these metrics over a salient parameter space, a linear, delayed vector autoregressive model was created with probabilistic and complex
coupling over probabilistic time delays. The model was run with various
measurement noise strengths, numbers of nodes, and number of available
data points. Correlation, cross-correlation, mutual information, multivariate
granger causality (MVGC), and transfer entropy (TE) were computed and
compared to true coupling adjacency matrices using an L-2 metric.
Significant differences were found between reconstruction results
between metrics. MVGC was found to outperform all other metrics
when the signal to noise ratio exceeded 0.23. Transfer entropy and correlation fared worse than maximum cross-correlation and mutual information, as summarized in Figure 1. Reconstruction error was found to
be minimally affected by number of nodes for metrics other than MVGC
and TE, where MVGC outperformed all others. Similarly, MVGC and TE
required a minimum number of samples to converge, and the required
number of points was found to be a function of the number of nodes.
Figure 1. Reconstruction error of time-lagged coupling as a function of measurement noise with standard deviations
BMC Neurosci 2017, 18(Suppl 1):60
Conclusions: Based on this work, significant disparity exists between
the performance of existing methods to detect delayed coupling.
Many common tools fail to detect delayed coupling. However, even
with a minimal density of time points to number of nodes, MVGC efficiently recovers complex and delayed coupling. Careful consideration
should be given to metrics used in experiments where coupling may
be delayed or spread out over time. Measurement noise and data sample density requirements may affect experimental design.
References
1. Netoff TI, Carroll TL, Pecora LM, Schiff SJ. 11 detecting coupling in the
presence of noise and nonlinearity. Handbook of Time Series Analysis: Recent
Theoretical Developments and Applications. John Wiley & Sons; 2006;
2. Barnett L, Seth AK. The MVGC multivariate Granger causality toolbox: a
new approach to Granger-causal inference. J. Neurosci. Methods. Elsevier;
2014;223:50–68.
3. Lindner M, Vicente R, Priesemann V, Wibral M. TRENTOOL: a Matlab open
source toolbox to analyse information flow in time series data with transfer
entropy. BMC Neurosci. BioMed Central Ltd; 2011;12:119.
4. Barnett L, Barrett AB, Seth AK. Granger causality and transfer entropy Are
equivalent for Gaussian variables. Phys. Rev. Lett. 2009;103:2–5.
P187
Insurgence of network bursting events in formed neuronal culture
networks: a computational approach
Davide Lonardoni1, Hayder Amin1, Stefano Di Marco2, Alessandro
Maccione1, Luca Berdondini1†, Thierry Nieus1,3†
1
Neuroscience and Brain Technology Department, Fondazione Istituto
Italiano di Tecnologia, Genova, Italy, 16163; 2Scienze cliniche applicate
e biotecnologiche, Università dell’Aquila, L’Aquila, Italy, 67100; 3Dept.
of Biomedical and Clinical Sciences “Luigi Sacco”, University of Milan,
Milan, Italy
Correspondence: Davide Lonardoni (davide.lonardoni@iit.it)
†
co-senior authors
BMC Neuroscience 2017, 18 (Suppl 1):P187
A common property of developing neuronal systems is their intrinsic ability to generate spatiotemporally propagating spiking activity
involving a large number of highly synchronously firing neurons. Primary neuronal cultures are among the experimental preparations that
allow the investigation of the principles underlying the generation
of such spontaneous coordinated spiking activity: cell cultures selforganize during development up to the stage where they elicit stereotyped network-wide spiking activity, called network bursts. The high
spatial resolution of the high-density CMOS multi-electrode arrays
revealed that network bursts correspond to a coordinated propagation of action potentials throughout the network [1]. Specifically, these
propagations could be well clustered into few groups differing for
their ignition sites (i.e. the starting point) and propagation paths (i.e.
the mean trajectory followed by the spiking activity) [2]. This finding
suggests the presence of regions in charge of triggering such spontaneous events. Following this direction, we investigated what were the
main determinants underlying the generation of network bursts in cell
cultures at the mature stage. To this end, we implemented a network
model made of principal cells (excitatory) and fast spiking (inhibitory)
neurons endowed with the proper synaptic currents (AMPA, NMDA,
GABA). With minimal topological constraints on the coupling between
neuronal pairs (i.e. a network structure based on the reciprocal distance among neurons), the model expressed realistic spontaneous
activities that mimicked the experimental findings.
The results obtained in this study, by combining experimental datasets
with our neural network computational model, shows that while the
synaptic contribution is mainly involved in shaping the network burst,
the key player in the generation of network bursts could be found in
the local properties of the neuronal network.
Specifically, with functional connectivity analysis, we found and
detected, both in simulation and in experiments, a few and specific
‘hot spots’ of the networks that matched with the ignition sites of
the propagations. In particular, in the model, the neurons of to the
hot spots were much more responsive than any other region to mild
stimulations delivered to these regions. Although the connectivity was
Page 111 of 176
truly uniform by design we found that the ‘hot spots’ were characterized by local graph properties (i.e. higher clustering, lower path length
respect to the remaining network) that favor the amplification of asynchronous firing and determine the onset of a network event. Our modeling study suggests that the ‘hot spots’ might naturally result from the
simple constraints on the network topology and the sparseness of the
network.
Acknowledgements
We acknowledge the financial support of the Future and Emerging
Technologies (FET) programme within the Seventh Framework Programme for Research of The European Commission for the SICODE
project, under FET-Open grant number: FP7-284553 and for the
NAMASEN Marie-Curie Initial Training Network, under FET-Open grant
number: FP7-264872.
References
1. Berdondini L, Imfeld K, Maccione A, Tedesco M, Neukom S, Koudelka-Hep
M, Martinoia S: Active pixel sensor array for high spatio-temporal resolution
electrophysiological recordings from single cell to large scale neuronal
networks. Lab Chip 2009, 9(18):2644–2651.
2. Gandolfo M, Maccione A, Tedesco M, Martinoia S, Berdondini L: Tracking
burst patterns in hippocampal cultures with high-density CMOS-MEAs. J
Neural Eng 2010, 7(5):056001.
P188
Brian2GeNN: Free GPU Acceleration for Brian 2 Users
Marcel Stimberg1, Dan F. M. Goodman2, Thomas Nowotny3
1
Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut de la
Vision, Paris, France; 2Department of Electrical and Electronic Engineering,
Imperial College, London, UK; 3School of Engineering and Informatics,
University of Sussex, Brighton, UK
Correspondence: Thomas Nowotny (t.nowotny@sussex.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P188
Over the last decade graphics processing units (GPUs) have evolved into
powerful, massively parallel co-processors that are increasingly used for
scientific computing and machine learning. But it has also become quite
clear that writing efficient code for GPU accelerators is difficult even
with APIs designed for general purpose computing, such as CUDA and
OpenCL. As a consequence, frameworks are being developed for making GPU acceleration available for specific applications without complex
parallel code design. Examples include Matlab GPU extensions [1], TensorFlow GPU support [2], Theano GPU extensions [3] and so on. Here we
present the first public release of Brian2GeNN [4], a software package
that connects the popular Brian 2 simulator [5] to the GPU enhanced
neuronal networks (GeNN) framework [6] to provide effortless GPU support for computational Neuroscience investigations to Brian 2 users.
Brian2GeNN was first announced at CNS*2014 and has undergone a long
phase of maturation and development until its first public release this
year. It is a Python based package that allows users to deploy their Brian
2 models to a device named “genn”, using the simple command “set_
device(‘genn’)”. This triggers the use of Brian2GeNN, which generates
code that can be executed on GPUs using GeNN. Brian2GeNN supports
all common features of Brian 2 with few exceptions such as multi-compartment models, multiple networks or heterogeneous delays.
On this poster, we present the basic principles of how Brian2GeNN
works and benchmark examples of its performance with a number of
different benchmark models and using a number of diverse GPU accelerators. We can demonstrate that depending on the model and the
accelerator, achieved speedups can vary considerably. Brian2genn is
Open Source and freely available on GitHub under GPL v2.
Acknowledgements
The development of Brian2GeNN was partially supported by EPSRC,
grant EP/J019690/1.
References
1. Mathworks web pages [https://uk.mathworks.com/company/newsletters/
articles/gpu-programming-in-matlab.html], accessed 03-03-2017.
2. TensorFlow web pages [https://www.tensorflow.org/tutorials/using_gpu],
accessed 03-03-2017.
BMC Neurosci 2017, 18(Suppl 1):60
3. Theano documentation [http://theano.readthedocs.io/en/latest/tutorial/
using_gpu.html], accessed 03-03-2017.
4. Brian2genn repository [https://github.com/brian-team/brian2genn],
accessed 03-03-2017
5. Stimberg M, Goodman DFM, Benichoux V, Brette R: Equation-oriented
specification of neural models for simulations. Front. Neuroinf. 2014, doi:
10.3389/fninf.2014.00006.
6. E. Yavuz, J. Turner and T. Nowotny (2016). GeNN: a code generation framework for accelerated brain simulations. Scientific Reports 2016, 6:18854.
doi: 10.1038/srep18854.
P189
Spike counts in the visual cortex consistently encode both stimuli
and behavioral choices in a change‑detection task
Veronika Koren1,2, Valentin Dragoi3, Klaus Obermayer1,2
1
Neural Information Processing Group, Institute of Software Engineering
and Theoretical Computer Science, Technische Universität Berlin, Berlin,
10587, Germany; 2Bernstein Center for Computational Neuroscience
Berlin, Berlin, Germany; 3Department of Neurobiology and Anatomy,
University of Texas Medical School, Houston, Texas, 77030, US
Correspondence: Veronika Koren (veronika.koren@ni.tu-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P189
In visual discrimination tasks, the subject collects information about
sensory stimuli and makes behavioral decisions accordingly. In this
study, we are searching for coding strategies in visual cortices of the
macaque (macaca mulatta) that relate to both stimuli and behavior. Multi-units within a single cortical column are recorded in V1 and
V4 areas simultaneously while the subject is performing a change
detection task with matching and non-matching stimuli. We assess
systematic differences in distribution of spike counts for matching vs.
non-matching stimuli (detection probability) and for correct vs. incorrect behavioral performance (choice probability, [1]) on the single cell
and on the population level. In addition, we estimate pair-wise correlations of spike counts. The spiking signal is weakly but significantly
predictive on the type of stimulus (matching vs. non-matching stimuli
with correct behavioral responses) as well as on different behavioral
choices with correct and incorrect behavioral performance (correct vs.
incorrect behavioral responses on non-matching stimuli). In both areas,
the effect is limited to the superficial layers of the cortical column.
Detection and choice probability are consistent, the behavioral choice
“match” being characterized by higher spike counts in both cases. In
V1, but not in V4, the signal corresponding to the choice”match” is even
statistically invariant with changes in both the type of the stimulus and
the behavioral performance. In incorrect trials, neural activity in V1 is in
addition characterized by a systematic bias in spike counts already at
the beginning of the trial. The bias is consistent with the future behavioral choice and is only present in the deep cortical layers. Comparing
the distribution of correlation coefficients across pairs of neurons with
matching and non-matching stimuli, distribution of coefficients in V4
is less variable with matching stimuli, in particular for short (0-0.5 mm)
and middle-range (0.5-1 mm) inter-neuron distances. This effect could
be interpreted as a fast adaptation of neural responses to two consecutive presentations of the same stimuli [2]. A change in long-range
(>1 mm) correlations in V4 is observed when comparing trials with correct and incorrect behavioral performance, correlations in incorrect trials showing higher variability. In V1, we did not observe any systematic
changes in spike-count correlations with different stimuli. However,
correlations are significantly more variable in trials with incorrect compared to correct behavioral performance. This effect is once again limited to deep cortical layers. Higher variability of correlations in V1 might
be a signature of spontaneously generated network state that is more
likely leading to incorrect behavioral performance. Finally, we test the
interactions between choice probabilities and spike-count correlations.
Choice probabilities and correlations do not interact in V1, but weakly
interact in the V4 area, where cells with similar choice probabilities tend
to be more strongly correlated. In summary, we observe various differences in the first and second order statistics of spike counts in both V1
and V4 areas. The first order statistics is related to coding of both stimuli
and behavioral choices while correlations would rather modulate the
efficacy of encoded signals.
Page 112 of 176
Acknowledgements
This work was supported by the Deutsche Forschungsgemeinschaft
(GRK1589/2).
References
1. Britten KH, Newsome WT, Shadlen MN, Celebrini S, Movshon JA: A relationship between behavioral choice and the visual responses of neurons in
macaque MT. Visual Neurosci 1996, 13(1):87–100.
2. Gutnisky DA, Dragoi V: Adaptive coding of visual information in neural
populations. Nature 2008, 452(7184): 220–224.
3. Hansen BJ, Chelaru MI, Dragoi V: Correlated variability in laminar cortical
circuits. Neuron 2012, 76(3): 590–602.
4. Nienborg H, Cumming BG: Decision-related activity in sensory neurons
may depend on the columnar architecture of cerebral cortex. J.Neurosci.
2014, 34(10): 3579–85.
P190
Local topology of connectome stabilizes critical points in mean field
model
Samy Castro1,2, Mariano Fernandez3, Wael El-Deredy4, Patricio Orio1,5
1
Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad
de Valparaíso, Valparaíso, 2360102, Chile; 2Programa de Doctorado en
Ciencias, mención en Neurociencia, Facultad de Ciencias, Universidad
de Valparaíso, Valparaíso, 2360102, Chile; 3Laboratorio de Electrónica
Industrial, Control e Instrumentación, Universidad Nacional de La Plata,
La Plata, Argentina; 4Escuela de Ingeniería Biomédica, Universidad
de Valparaíso, 2362905, Valparaíso, Chile; 5Instituto de Neurociencia,
Universidad de Valparaíso, Facultad de Ciencias, Universidad de
Valparaíso, Valparaíso, 2360102, Chile
Correspondence: Samy Castro (samy.castro@cinv.cl)
BMC Neuroscience 2017, 18 (Suppl 1):P190
The interplay between structural connectivity (SC) and neural dynamics
is still not yet fully understood. Applying topological analysis, the connectome approach links this anatomical network to brain function. Here
we adopt a computational approach to find topology features related
to the stability on global neural dynamics. A previous study of a mean
field model based on the human cortex network, shows at least 2 global
neural states, with either a low or high firing rate pattern [1, 3]. These 2
possible states, or bistability, emerge in the model within a range of the
global coupling parameter G, limited by critical values G- and G+[1, 3].
Also, at this bistable range, this model achieves the highest correlations
with empirical resting state fMRI data. How the network connectivity pattern shapes the critical G values has not been yet investigated. Our aim is
to identify local or global topology features related to the critical G values. We studied 4 different SC networks: a cortical parcellation of human
brain [2], a human binary equivalent, a Random Network (RN) having
the same degree distribution as human SC, and an equivalent Watts &
Strogatz Small World (SW) network. For each of the analyzed networks,
values in their critical G points have small or null variability. Then, we
selectively prune the edges of the networks and calculate their critical G
values to show the effect of structure pattern in maintaining the bistable
dynamics. The edges were pruned selectively based on either the degree
or the k core decomposition measure; interpreted as a local or global
topology feature, respectively. Also, the pruning procedure is applied
to the edges on one of 3 specific ways: i) high degree/k core nodes, ii)
random cuts, and iii) low degree/no k core nodes. The highest shifts in
critical G values are achieved when the edges of high degree or k core
nodes are pruned. In contrast, when we prune those edges belong to low
degree or no k core nodes, the shifts in the critical G points are irrelevant.
We interpret this as that the model can use either local or global connectivity pattern in order to stabilize the critical G points. Furthermore, our
study show that shifts in the critical G points are statistically equivalent
when the degree distribution (but not k core structure) is shared, such as
in the binary human SC compared to the RN. Therefore, in our simulation
the degree distribution, interpreted as a local connectivity feature, determines the critical G points for bistability, capturing the essential structural pattern of the network. We also show that it is possible to obtain
bistability in other types of networks, suggesting that structure dynamic
relationships may obey a topological principle.
BMC Neurosci 2017, 18(Suppl 1):60
Acknowledgements
SC is recipient of a Ph.D. fellowship from CONICYT. PO is partially
funded by the Advanced Center for Electronic Engineering (FB0008
CONICYT, Chile). The Centro Interdisciplinario de Neurociencia de Valparaíso (CINV) is a Millennium Institute supported by the Millennium
Scientific Initiative of the Ministerio de Economía (Chile).
Page 113 of 176
Acknowledgements
KX is funded by Proyecto Fondecyt 3170342. PO is partially funded by
the Advanced Center for Electrical and Electronic Engineering (FB0008
Conicyt, Chile). The Centro Interdisciplinario de Neurociencia de Valparaíso (CINV) is a Millennium Institute supported by the Millennium
Scientific Initiative of the Ministerio de Economía (Chile).
References
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Hagmann P, Jirsa VK: Identification of optimal structural connectivity
using functional connectivity and neural modeling. J Neurosci. 2014,
34(23):7910–7916.
2. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Van Wedeen J,
Sporns O: Mapping the structural core of human cerebral cortex. PLoS Biol.
2008, 6(7):1479–1493.
3. Deco G, Ponce-Alvarez A, Mantini D, Romani GL, Hagmann P, Corbetta
M: Resting-state functional connectivity emerges from structurally and
dynamically shaped slow linear fluctuations. J Neurosci. 2013, 33(27):
11239–11252.
1. Orio P., Parra A., Madrid R., González O., Belmonte C., Viana F. Role of Ih in
the Firing Pattern of Mammalian Cold Thermoreceptors. J Neurophysiol
2012, 108:3009–3023
2. Xu K., Maidana JP, Caviedes M, Quero D, Aguirre P and Orio P. Hyperpolarization-activated current induces period-doubling cascades and chaos in a
cold thermoreceptor model. Front Comput Neurosci 2017, 11:12.
doi: 10.3389/fncom.2017.00012.
P191
How chaos in neural oscillators determine network behavior
Kesheng Xu1, Jean Paul Maidana1, Patricio Orio1,2
1
Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad
de Valparaíso, Valparaíso, Chile; 2Facultad de Ciencias, Instituto de
Neurociencia, Universidad de Valparaíso, Valparaíso, Chile
Correspondence: Patricio Orio (patricio.orio@uv.cl)
BMC Neuroscience 2017, 18 (Suppl 1):P191
Chaotic dynamics of neural oscillations has been shown at the single
neuron and network levels, both in experimental data and numerical
simulations. Theoretical works suggest that chaotic dynamics enrich
the behavior of neural systems, by providing multiple attractors in a
system. However, the contribution of chaotic neural oscillators to relevant network behavior has not been systematically studied yet. We
investigated the synchronization of neural networks composed of conductance-based neural models that display subthreshold oscillations
with regular and burst firing [1]. In this model, oscillations are driven
by a combination of persistent Sodium current, a hyperpolarizationactivated current (Ih) and a calcium-activated potassium current,
very common currents in the CNS. By small changes in conductance
densities, the model can be turned into either chaotic or non-chaotic
modes [2]. We study synchronization of heterogeneous networks
where conductance densities are drawn from either chaotic or nonchaotic regions of the parameter space. Measuring mean phase synchronization in a small-world network with electrical synapses, we
characterize the transition from unsynchronized to synchronized state
as the connectivity strength is increased. First, we draw densities from
fixed-size regions of the parameter space and find the transition to
synchronized oscillations is always smooth for chaotic oscillators but
not always smooth for the nonchaotic ones. However, non-smooth
transitions were found to be associated to a change in firing pattern
from tonic to bursting. Nevertheless, we noticed that chaotic oscillators display a wider distribution of firing frequencies than non-chaotic
oscillators, thus making more heterogeneous networks. Next, we draw
the conductance densities from the parameter space in a way that
maintained the same distribution of firing frequencies (hence the
heterogeneity of the network) for both chaotic and non-chaotic. In
this case, synchronization curves are very similar, being second order
phase transition for both cases. However, we cannot discard that nonchaotic oscillators become chaotic (or vice versa) when in a network,
because of the extra parameter associated to the electrical synapse.
Finally, when the chaos-inducing Ih current is removed, the transition
to synchrony occurs at a lower value of connectivity strength but with
a similar slope.
Our results suggest that the chaotic nature of the individual oscillators
may be of minor importance to the synchronization behavior of the
network. Ongoing work is being conducted to measure the chaotic
nature of the whole network, and how it is related to the synchrony
behavior.
P192
STEPS 3: integrating stochastic molecular and electrophysiological
neuron models in parallel simulation
Weiliang Chen1, Iain Hepburn1, Francesco Casalegno2, Adrien Devresse2,
Aleksandr Ovcharenko2, Fernando Pereira2, Fabien Delalondre2, Erik De
Schutter1
1
Computational Neuroscience Unit, Okinawa Institute of Science
and Technology Graduate University, Okinawa, Japan; 2Blue Brain Project,
École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Correspondence: Weiliang Chen (w.chen@oist.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P192
Stochastic spatial molecular reaction-diffusion simulators, such as
STEPS (STochastic Engine for Pathway Simulation) [1], often face great
challenges when simulating large scale complex neuronal pathways,
due to the massive computation required by the models. This issue
becomes even more critical when combining with cellular electrophysiological simulation, one of the main focuses in computational
neuroscience research. One example is our previous research on stochastic calcium dynamics in Purkinje cells [2], where a biophysical calcium burst model was simulated on approximate ¼ of a Purkinje cell
dendritic tree morphology using the serial implementation of spatial
Gillespie SSA and electric field (EField) solver in STEPS 2.0. Even with
a state-of-the-art desktop computer, it still took months to finish the
simulation, significantly slowing down research progress.
One possible, yet not trivial approach to speedup such simulation
is parallelization. In CNS2016 we reported our early parallel implementation of an Operator-Splitting solution for reaction-diffusion
systems, which achieved super-linear speedup in simulation of the
buffer components of the above published model on full Purkinje
cell morphology. While the performance of our parallel implementation was promising, the test model had no calcium presented in the
system and only buffers were simulated. Since buffers were uniformly
distributed in the geometry, the loading of each computing process
was relatively balanced, resulting in a close to ideal scenario for parallel computation. The membrane potential computation, as well as
voltage-dependent reactions in the published model, were omitted
due to the lack of a parallel EField solver at the time. In a recent publication [3], we further extended the model by applying a dynamically
updated calcium influx profile extracted from the published calcium
burst simulation. Our result shown that in a realistic scenario with
dynamic calcium influx, data recording, and without special load balancing, our parallel reaction-diffusion solution can still achieve more
than 500 times of speedup with 1000 computing processes comparing
to the conventional serial SSA solution.
STEPS 3 is the first public release out of the collaboration between the
CNS Unit of OIST and the Blue Brain Project of EPFL. The ongoing collaboration aims to deliver a scalable parallel solution for future integrated
stochastic molecular and electrophysiological neuron modelling. Combining the parallel TetOpSplit molecular solver developed by OIST and
EPFL’s parallel EField solver based upon the PETSc library, our new
release addresses the limitations of above test cases, and allows full
scale parallel simulation of the complete Purkinje cell calcium burst
model. It also contains new changes that are essential to parallel STEPS
BMC Neurosci 2017, 18(Suppl 1):60
modelling and simulation pipeline, such as the improved python binding using Cython technology. In this poster, we will use this model as
an example to showcase the general procedure of converting a serial
STEPS simulation to its parallel counterpart using these new changes.
We will also analyze the performance and scalability of our integrated
solution, and discuss the direction of future STEPS development.
References
1. Hepburn, I., Chen, W., Wils, S., and De Schutter, E. (2012). STEPS: efficient
simulation of stochastic reaction–diffusion models in realistic morphologies. BMC Systems Biology 6, 36. doi:10.1186/1752-0509-6-36.
2. Anwar, H., Hepburn, I., Nedelescu, H., Chen, W., and De Schutter, E. (2013).
Stochastic calcium mechanisms cause dendritic calcium spike variability. J.
Neurosci. 33, 15848–15867. doi:10.1523/JNEUROSCI.1722-13.2013.
3. Chen, W., and De Schutter, E. (2017). Parallel STEPS: Large Scale Stochastic
Spatial Reaction-Diffusion Simulation with High Performance Computers.
Front. Neuroinform. 11, 137–15. doi:10.3389/fninf.2017.00013.
P193
A conductance‑based model of cerebellar molecular layer
interneurons
Peter Bratby1, Erik de Schutter1
1
Okinawa Institute of Science and Technology Graduate University,
1919-1 Tancha, Onna-son, Kunigami-gun, Okinawa 904-0495, Japan
Correspondence: Peter Bratby (peter.bratby@oist.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P193
The cortex of the cerebellum is one of the most well-characterized
regions of the brain, comprising three distinct layers whose connectivity is well understood. Numerical simulations of parts of the cerebellar
cortex, including the granular layer and Purkinje cell layer, have been
instrumental in revealing the computational properties of the cerebellum. However, one important part of the cortex - the molecular layer
- has yet to be modeled in detail.
The molecular layer is comprised of many thousands of parallel fibers
(the long unmyelinated axons of granule cells), Purkinje cell dendrites
and a network of inhibitory interneurons termed stellate cells and basket cells. The inhibitory interneurons were originally classified according to their morphology, although modern molecular techniques have
indicated that they are likely to belong to a single class of neuron,
the molecular layer interneuron (MLI). As well as forming excitatory
connections onto Purkinje cells, parallel fibers make disynaptic connections via MLIs. Furthermore, MLIs form chemical and electrical connections with each other via GABAergic synapses and gap junctions.
Thus, the MLIs form a sophisticated inhibitory network whose properties are important in shaping the output of the cerebellum itself.
We develop a detailed conductance-based model of an MLI, and present the results of a simulation of a small MLI network. The neuron
model, developed using NEURON simulation software, comprises
somatic and dendritic compartments containing distinct voltage- and
calcium-dependent ion channels. Two types of synapse are simulated,
representing chemical synapses and gap junctions. The connectivity
and cellular geometry of the network model conforms with morphological reconstructions, and the model parameters were tuned in order
to reproduce known electrophysiological properties of MLIs, including
spontaneous spiking activity, modest spike frequency adaptation and
the presence of a slow depolarization wave.
P194
An Ultrasensitive ON/OFF Switch Mechanism Controls the Early
Phase of Cerebellar Plasticity
Andrew R. Gallimore, Erik De Schutter
Computational Neuroscience Unit, Okinawa Institute of Science
and Technology Graduate University, Onna-son, Okinawa, Japan
Correspondence: Andrew R. Gallimore (andrew.gallimore@oist.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P194
The expression of postsynaptic long-term depression (LTD) and
long-term potentiation (LTP) in cerebellar Purkinje cells results from
the internalisation or insertion, respectively, of postsynaptic AMPA
receptors (AMPAR) [1]. LTD is induced by concurrent parallel fiber
Page 114 of 176
and climbing fiber stimulation of Purkinje cells, and is regulated by a
complex intracellular signaling network that suppresses phosphatase
activity leading to activation of a positive feedback loop that maintains
PKC activity for at least 30 min [2]. LTP is dependent on nitric oxide
[3], produced during parallel fiber stimulation [4], which nitrosylates
N-ethylmaleimide-sensitive factor (NSF) and promotes exocytosis of
AMPARs by actively disrupting the interaction between AMPAR-GluR2
and protein interacting with C-kinase 1 (PICK-1) [5, 6].
We report the largest and most sophisticated model of bidirectional
synaptic plasticity to date at the PF-PC synapse. Our unified molecular
model replicates both PF-PC LTD and NO/NSF-dependent LTP, as well as
the sharp calcium threshold separating them. The importance of the positive feedback loop in LTD expression is now well-established. However,
the control of feedback loop activation and deactivation has, until now,
remained obscure. Model simulations reveal that the feedback loop is
activated by an ultrasensitive ‘on-switch’ controlled by CaMKII activation.
Furthermore, as predicted by experiments showing that the feedback
loop is not required once the early phase of LTD induction is complete
[2, 7], our model reveals a rapid and automatic ‘switch-off’ mechanism
controlled by phosphatase activity. We are also able to replicate several
experimental observations that have so far remained unexplained. These
include reconciling conflicting data regarding the importance of nitric
oxide in LTD induction: nitric oxide supports loop activation by augmenting phosphatase inhibition, but is not required when the calcium signal
is high or sustained [4]. In addition, experiment has shown that selective
inhibition of the cytosolic phosphatase, PP2A, elicits robust LTD, whereas
inhibition of other phosphatases does not [8]. We show that only PP2A
inhibition causes CaMKII-independent activation of the feedback loop
and thus LTD induction, revealing the importance of PP2A in suppressing
spontaneous loop activation under basal conditions.
References
1. Wang YT, Linden DJ: Expression of cerebellar long-term depression requires
postsynaptic clathrin-mediated endocytosis. Neuron 2000, 25(3):635–647.
2. Tanaka K, Augustine GJ: A positive feedback signal transduction loop
determines timing of cerebellar long-term depression. Neuron 2008,
59(4):608–620.
3. Lev-Ram V, Wong ST, Storm DR, Tsien RY: A new form of cerebellar longterm potentiation is postsynaptic and depends on nitric oxide but not
cAMP. Proceedings of the National Academy of Sciences of the United States of
America 2002, 99(12):8389–8393.
4. Bouvier G, Higgins D, Spolidoro M, Carrel D, Mathieu B, Lena C, Dieudonne
S, Barbour B, Brunel N, Casado M: Burst-Dependent Bidirectional Plasticity
in the Cerebellum Is Driven by Presynaptic NMDA Receptors. Cell Reports
2016, 15(1):104–116.
5. Huang Y, Man HY, Sekine-Aizawa Y, Han YF, Juluri K, Luo HB, Cheah J,
Lowenstein C, Huganir RL, Snyder SH: S-nitrosylation of N-ethylmaleimide
sensitive factor mediates surface expression of AMPA receptors. Neuron
2005, 46(4):533–540.
6. Hanley JG, Khatri L, Hanson PI, Ziff EB: NSF ATPase and alpha-/beta-SNAPs
disassemble the AMPA receptor-PICK1 complex. Neuron 2002, 34(1):53–67.
7. Tsuruno S, Hirano T: Persistent activation of protein kinase C alpha is not
necessary for expression of cerebellar long-term depression. Molecular and
Cellular Neuroscience 2007, 35(1):38–48.
8. Launey T, Endo S, Sakai R, Harano J, Ito M: Protein phosphatase 2A inhibition induces cerebellar long-term depression and declustering of synaptic
AMPA receptor. Proceedings of the National Academy of Sciences of the United
States of America 2004, 101(2):676–681.
P195
The use of hardware accelerators in the STochastic Engine
for Pathway Simulation (STEPS)
Guido Klingbeil, Erik de Schutter
Computational Neuroscience Unit, Okinawa Institute of Science
and Technology, 1919-1 Tancha, Onna-son, Kunigami-gun, Okinawa
904-0495, Japan
Correspondence: Guido Klingbeil (guido-klingbeil@oist.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P195
STEPS is a stochastic reaction-diffusion simulator. Its emphasis is on
accurately simulating signaling pathways [1].
BMC Neurosci 2017, 18(Suppl 1):60
Page 115 of 176
The Human Brain Project (HBP) is a European Project set out to gain
long-sought insights into our brain and the processes that fundamentally make us human. A parallelised version of STEPS will be part of the
Brain Simulation Platform of the Human Brain Project by efficiently
simulating reaction-diffusion models in realistic morphologies [2].
The HPB will model the brain at unprecedented detail. It is becoming
apparent that such large scale and computationally expensive models
are required to either capture more realistic morphologies or to simulate more complex systems [3].
Hardware accelerators such as NVidia’s graphics processing units (GPU)
or Intel’s Xeon Phi are one approach to mitigate the high computational cost of such models. They are, in general, massively parallel multicore co-processors and have become a cornerstone of modern high
performance computing [4].
The hardware architecture of these two accelerator families differ significantly and thus require different software approaches. While both
are programmable via the common programming interface OpenCL,
important features such as unified memory or remote direct memory
access (RDMA) are often only supported in the native hardware architecture specific programming frameworks [5, 6]. These not only need
to be integrated into an overall parallel software system performing
a coherent spatial simulation but also need to scale well over several
accelerators and compute nodes.
Previous research has shown that we can exploit the computational
power of accelerators to improve spatially homogenous stochastic
simulations by two orders of magnitude while avoiding the limitation imposed to the size of the reaction system to be simulated by the
small fast memory space [7].
STEPS implements a spatial version of Gillespie’s stochastic simulation
algorithm computing reaction-diffusion systems on a mesh of tetrahedral sub-volumes [1, 8]. Operator splitting techniques allow to separate the reaction of molecules within a sub-volume from the diffusion
of molecules between them.
We develop a layered hybrid software architecture using classic central
processing units as well as multiple accelerators, integrated into STEPS.
Multiple sub-volumes are assigned to an accelerator. To accommodate
the different hardware characteristics, NVidia GPUs are applied within
a sub-volume and the Intel Xeon Phi at the level of the operator splittings. Furthermore, due to differences in the performance characteristics of the accelerators the use of load balancing at the tetrahedral
mesh level will be important.
Our architecture will be a plug-in solution to STEPS not requiring any
changes to the interfaces towards the user or other software systems
of STEPS itself.
Cerebellar Long Term Depression (LTD) is a form of synaptic plasticity involved in motor learning. The LTD signaling network
includes a PKC-ERK-cPLA2 positive feedback loop and mechanisms
of AMPAR receptor trafficking. Experimental studies suggest that
Ca2+/calmodulin-dependent protein kinase II (CaMKII) is required
for the LTD induction [1]. Additionally, theoretical and experimental work has shown that CaMKII is sensitive to the frequency of
Ca2+ oscillations [2, 3]. The activation and autophosphorylation
of CaMKII by Ca2+ and calmodulin (CaM) are thought to influence
its ability to decode Ca2+ oscillations. However, the molecular
mechanism by which this sensitivity contributes to LTD is not fully
understood.
The CaMKII enzyme is a multimeric complex conformed by 12 subunits, each of which contains a catalytic domain, a regulatory domain,
and a carboxyl-terminal association domain. Due to the combinatorial complexity of activation of this enzyme, we chose to model
four-subunits. We propose a model for the activation of CaMKII by
Ca2+ in LTD signaling network. These reactions include: activation
of the enzyme by Ca2+/CaM binding, intersubunit autophosphorylation at threonine residue Thr286, Ca2+-independent activation
state through autophosphorylation and secondary intersubunit
autophosphorylation at threonine residue Thr305/306. Noise in the
signaling networks plays an important role in cellular processes.
CaMKII models including its activation have been developed [3],
but they have not included the intrinsic stochasticity of molecular
interactions.
Our lab recently developed a stochastic model of the LTD signaling
network including a PKC-ERK-cPLA2 feedback loop, Raf-RKIP-MEK
interactions and AMPAR trafficking [4]. We have extended this model
by adding the molecular network regulating CaMKII activity and its
activation. This new model was solved stochastically by STEPS (STochastic Engine for Pathway Simulation) [5] to simulate the influence of
noise on the LTD signaling network.
Through stochastic modeling we observed that CaMKII can decode
the frequency of Ca2+ spikes into different amounts of kinase activity
during LTD induction. This result is congruent with previous studies of
CaMKII sensitivity to Ca2+ oscillations [2]. Furthermore, we observed
that PKC activity is highly sensitive to the frequency, amplitude, duration and the number of Ca2+ oscillations and consequently has an
important effect on LTD activation. The LTD signaling network involves
phosphatases and phosphodiesterases related with CaMKII activity,
such as PP2A and PDE1. Our stochastic model may be useful in understanding the role of these enzymes in the CaMKII sensitivity to the frequency of Ca2+ oscillations.
References
1. Hepburn et al.: STEPS: efficient simulation of stochastic reaction-diffusion
models in realistic morphologies. BMC Syst Bio 2012, 6:36.
2. The Human Brain Project Brain Simulation Platform [https://www.humanbrainproject.eu/brain-simulation-platform1].
3. Anwar et al.: Stochastic Calcium Mechanisms Cause Dendritic Calcium
Spike Variability. J Neurosci 2013, 33(40):15848–15867.
4. TOP500 Supercomputer Site [http://www.top500.orgError! Hyperlink reference not valid.].
5. Khronos OpenCL Working Group: The OpenCL Specification, V 2.1, 2015.
6. NVidia: CUDA C programming guide, V 8.0, 2017, [https://developer.nvidia.
com/cuda-toolkit].
7. Klingbeil et al.: Stochastic simulation of chemical reactions with cooperating
threads on GPUs. (in preparation).
8. Gillespie: Exact stochastic simulation of coupled chemical reactions. J Phys
Chem 1977, 81(25):2340–2361.
References
1. Hansel C, de Jeu M, Belmeguenai A, Houtman SH, Buitendijk GH, Andreev D,
De Zeeuw CI, Elgersma Y: αCaMKII is essential for cerebellar LTD and motor
learning. Neuron 2006, 51:835–843.
2. Paul De Koninck and Howard Schulman: Sensitivity of CaM Kinase II to the
Frequency of Ca2 + Oscillations. Science 1998, 279: 227–230.
3. Geneviève Dupont, Gerald Houart, Paul De Koninck: Sensitivity of CaM
Kinase II to the Frequency of Ca2 + Oscillations: a simple model. Cell
Calcium 2003, 34: 485–497
4. Iain Hepburn, Anant Jain, Himanshu Gangal, Yukio Yamamoto, Keiko
Tanaka-Yamamoto and Erik de Schutter. A Model of Induction of Cerebellar
Long-Term Depression Including RKIP Inactivation of Raf and MEK. Front
Mol Neurosci 2017, 10: 19.
5. Hepburn I, Chen W, Wils S, De Schutter E: STEPS: efficient simulation of
stochastic reaction-diffusion models in realistic morphologies. BMC Syst Biol
2012, 6:36.
P196
A model of CaMKII sensitivity to the frequency of Ca2+ oscillations
in Cerebellar Long Term Depression
Criseida Zamora and Erik De Schutter
Computational Neuroscience Unit, Okinawa Institute of Science
and Technology Graduate University, Okinawa 904-0895, Japan
Correspondence: Criseida Zamora (criseida.chimal@oist.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P196
P197
Exploring the response to climbing fiber input in Purkinje neurons
by a new experimental data based model
Yunliang Zang, Erik De Schutter
Computational Neuroscience Unit, Okinawa Institute of Science
and Technology Graduate University, Onna-son, Okinawa, Japan
Correspondence: Yunliang Zang (yunliang.zang@oist.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P197
BMC Neurosci 2017, 18(Suppl 1):60
Purkinje neurons receive powerful climbing fiber (CF) input from
Inferior Olive (IO) neurons to provide an instructive signal for cerebellar learning. The initial observation that CF input causes all or none
responses has been questioned in recent years. However, the mechanisms of initiation and propagation of dendritic calcium spikes evoked
by CF input are still poorly understood. Here, we build a new Purkinje
cell model based on available experimental data to explore dendritic
and somatic responses to CF input in the Purkinje cell under different
conditions. All the ionic current models are well constrained according
to the experimental data.
Model ionic currents regulate the electrophysiological properties
of the Purkinje cell consistent with experimental observations. Our
model reproduces a plethora of experimental observations, properties that are critical for the model to be able to predict responses to
excitatory and inhibitory inputs. Both simple spike and complex spikes
initiate first in the axonal initial segment (AIS). The first derivative and
second derivative of the somatic simple spike are in agreement with
experimental data.
Using this model, we can explain the discrepancies between experimental observations from different groups about the spatial propagation range of dendritic calcium spikes. Dendritic spikelets can initiate
and propagate in a branch-specific manner and depolarization of dendrites can cause secondary spikelets. We find that the timing of occurrence of a spikelet is critical to determine whether it can affect somatic
firing or not. The branch-specific dendritic spikelets can combine with
contaminant excitatory input and inhibitory inputs to affect somatic
firing output more efficiently. Our results indicate that voltagedependent and branch specific spikelets may enrich CF instructive signals for cerebellar learning.
P198
Effects of network topology perturbations on memory capacity in a
hippocampal place cell model
Patrick Crotty, Eric Palmerduca
Department of Physics and Astronomy, Colgate University, Hamilton, NY
13346, USA
Correspondence: Patrick Crotty (pcrotty@colgate.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P198
The relationship between the structure, or topology, of a neural network and its dynamics remains largely unexplored. This relationship
may be particularly significant for the place cell network in region
CA3 of the hippocampus. Place cells are believed to encode position
by firing when the animal is in a specific spatial location [1]. Multiple
“charts” mapping place cells to locations for several different environments may be stored simultaneously in the network [2]. Given hippocampal neurogenesis and synaptic plasticity, the place cell network
should be robust to small perturbations in its topology: it shouldn’t
“forget” charts if the pattern of synaptic connections changes slightly.
Conversely, if Alzheimer’s or another neurodegenerative disease
attacks the place cell network, declines in the chart capacity could
provide clues about the presence and progression of the disease.
Using a computational model based on a place cell network model
published by Azizi et al. [3], we investigated the effects that random
removal of synapses in the network had on chart capacity. When
small numbers of synapses were removed, the chart capacity was not
measurably affected, but larger numbers removed caused the chart
capacity to decline (see the Figure 1). Moreover, the decline in the
chart capacity depended on how the synapses were selected. If they
were selected with uniform probability, the chart capacity remained
unaffected out to about 10% removed and then fell sharply. But if
neurons, rather than synapses, were first selected with uniform probability, and then synapses randomly removed from the selected neurons, the chart capacity began to fall linearly at about 5% removed.
These results suggest that the place cell network chart capacity is
indeed stable to small perturbations in its topology, and that the
effects of larger disruptions depend on the underlying mechanisms,
i.e., whether it is the synapses or the cells themselves that are targeted by a disease.
Page 116 of 176
Figure 1. The chart capacity (M) as a function of the fraction of the
synapses removed from the network (p), using two different synapse-removal algorithms. For the blue curve, synapses are selected
and removed with equal probability. For the red curve, neurons
are selected with equal probability, and then a random synapse is
removed from the selected neuron. The dashed line is a linear fit to
the random-neuron (red) curve, with slope -0.27
Acknowledgements
We thank A. Azizi and S. Cheng for helpful discussions.
References
1. O’Keefe J, Dostrovsky J: The hippocampus as a spatial map: preliminary
evidence from unit activity in the freely-moving rat. Brain Research 1971,
34:171–175.
2. Alme CB, Miao C, Jezek K, Treves A, Moser EI, and Moser M: Place cells in the
hippocampus: eleven maps for eleven rooms. Proceedings of the National
Academy of Sciences 2015, 111(52):18428–18435.
3. Azizi A, Wiskott L, Cheng S: A computational model for preplay in the hippocampus. Frontiers in Computational Neuroscience 2013, 7(161):1–15.
P199
A NEST‑simulated cerebellar spiking neural network driving motor
learning
Alberto Antonietti1, Claudia Casellato1, Csaba Erö2, Egidio D’Angelo3,
Marc-Oliver Gewaltig2, Alessandra Pedrocchi1
1
Department of Electronics, Information and Bioengineering, Politecnico
di Milano, Milano, Italy; 2Blue Brain Project, Ecole Polytechnique Fédérale
de Lausanne (EPFL), Biotech Campus, Geneva, Switzerland; 3Department
of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
Correspondence: Alberto Antonietti (alberto.antonietti@polimi.it)
BMC Neuroscience 2017, 18 (Suppl 1):P199
The brain organization is optimized to drive adaptive behavior. A key
role in the control loop is played by the cerebellum, which implements
prediction, timing and learning of motor commands, through complex
plasticity mechanisms [1]. However, how plasticity is engaged during
the behavior is still unclear. Cerebellar properties emerge in sensorimotor paradigms, such as the Eye Blink Classical Conditioning (EBCC). In
silico simulations based on computational models are fundamental to
investigate the physiological mechanisms. We developed a cerebellar
network running on NEST. NEST is a simulator for spiking neural network
models [2], focused on the dynamics, size and structure of neural systems by the generation of networks of single-point neurons. We built
a network tailored on the mouse cerebellum. The network is made of
71,440 neurons: 250 Mossy Fibers (MF), 5’000 Glomeruli (Glom), 65’600
Granular Cells (GR), 100 Golgi Cells (GO), 400 Purkinje Cells (PC), 40 Inferior Olive cells (IO), 50 Deep Cerebellar Nuclei (DCN). The connectivity
BMC Neurosci 2017, 18(Suppl 1):60
Page 117 of 176
ratios used for the 11 types of synaptic connections are reported in
Table 1. Three of these synaptic types could undergo specific plastic
modifications, in particular Long Term Potentiation and Depression on
different time scales. The numbers of the cells and the connectivity were
taken from the neurophysiological literature. The model was tested with
a simple closed-loop simulation of the EBCC, to check the functionalities
of the network in a learning task [3]. In the EBCC, a Conditioned Stimulus
(CS) precedes an Unconditioned Stimulus (US) by a fixed time interval.
The cerebellum is able, after repeated presentations of CS and US paired
during the acquisition phase, to anticipate the US onset, this action is
called Conditioned Response (CR). During the extinction phase, only
the CS is provided. The network, thanks to the distributed plasticity, was
able to learn the CS-US temporal association during the acquisition trials, with a fast acquisition towards 80% values, and to rapidly unlearn
the association during the extinction trials (Figure 1). We will extend this
model to a large-scale reproduction of the mouse cerebellum, testing
more complex paradigms.
Table 1. Connectivity between the neural groups (Convergence
and Divergence). In italics the plastic sites
Presyn
Postsyn
Type
MF
Glom
Glom
GR
Glom
Div
# Synapses
Excitatory 1
20
5,000
Excitatory 4
53
262,400
GO
Excitatory 40
0.77
4,000
GO
GR
Inhibitory
2120
212,000
GR
GO
Excitatory 2000
3
200,000
GR
PC
Excitatory
65600
400
26,240,000
IO
PC
Teaching
1
10
400
PC
DCN
Inhibitory
40
5
2,000
DCN (30%) IO
Inhibitory
0.34
1
14
IO
DCN
Excitatory 1
1,41
50
MF
DCN
Excitatory
2.4
600
Total Number of Synapses
Conv
3.23
12
26,926,464
Figure 1. Behavioral outcome during the EBCC protocol, with 80
trials of Acquisition and 20 trials of Extinction. 10 simulations were
performed. Solid line: the median outcome; grey area: the interquartile intervals
Acknowledgements
This work was supported by EU grants: Human Brain Project (HBP
604102) and HBP-Regione Lombardia.
References
1. D’Angelo E et al.: Modeling the Cerebellar Microcircuit: New Strategies for a
Long-Standing Issue. Front. Cell. Neurosci. 2016; 10:176.
2. Gewaltig MO and Diesmann M: NEST (neural simulation tool). Scholarpedia
2007, 2(4):14303.
3. Antonietti et al.: Spiking Neural Network With Distributed Plasticity Reproduces Cerebellar Learning in Eye Blink Conditioning Paradigms. IEEE Trans.
Biomed. Eng. 2016. 63:1.210–219.
P200
Spike‑based probabilistic inference with correlated noise
Ilja Bytschok1, Dominik Dold1, Johannes Schemmel1, Karlheinz Meier1,
Mihai A. Petrovici1,2
1
Kirchhoff-Institute for Physics, Heidelberg University, Im Neuenheimer
Feld 227, 69120 Heidelberg, Germany; 2Department of Physiology,
University of Bern, Bühlplatz 5, 3012 Bern, Switzerland
Correspondence: Ilja Bytschok (ilja.bytschok@kip.uni-heidelberg.de)
BMC Neuroscience 2017, 18 (Suppl 1):P200
It has long been hypothesized that the trial-to-trial variability in neural activity patterns plays an important role in neural information processing. A steadily increasing body of evidence suggests that the brain
performs probabilistic inference to interpret and respond to sensory
input [1, 2, 3]. The neural sampling hypothesis [4] interprets stochastic
neural activity as sampling from an underlying probability distribution and has been shown to be compatible with biologically observed
dynamical regimes of spiking neurons [5]. In these studies, highfrequency Poisson spike trains were used as a source of stochasticity,
which is a common way of representing diffuse synaptic input. However, this discounts the fact that cortical neurons may share a significant portion of their presynaptic partners, which can have a profound
impact on the computation these neurons are required to perform.
This is not only relevant in biology, but also for artificial implementations of neural networks [6], where bandwidth constraints limit the
amount of available independent noise channels.
In neural sampling, the firing activity of a network of N Leaky Integrate-and-Fire (LIF) neurons is represented by a vector of binary random variables (RVs) z ∊ {0, 1}N. In such a network, synaptic weights can
be adjusted such that the network samples from a Boltzmann distribution p(z) [5]. In particular, the weights Wij control the pairwise correlations rij between RVs. When receiving correlated noise, the correlations
rij are changed in a way that cannot be directly countered by changes
in Wij. We show, however, that this is contingent on the chosen coding: when changing the state space from {0, 1}N to {−1, 1}N, correlated
noise has the exact same effect as changes in W. Unfortunately, the
{−1, 1}-coding is incompatible with neuronal dynamics, because it
would require neurons to influence each other while they are silent.
However, the translation of the problem to the {−1, 1}N space allows
the formulation of a two-step compensation procedure. We show
how, by chaining a bijective map from noise correlations to interaction strengths W’ijin {−1, 1}N with a second bijective map from (W’ij, b’ij) in
{−1, 1}N to (Wij, bij) in {0, 1}N it is possible to find a synaptic weight configuration that compensates for correlations induced by shared noise
sources. For an artificial embedding of sampling networks, this allows
a straightforward transfer between platforms with different architecture and bandwidth constraints.
Furthermore, the existence of the above mapping provides an important
insight for learning. Since in the {−1, 1}-coding the correlated noise can
be compensated by parameter changes and because the {−1, 1}-coding
can be transformed into a {0, 1}-coding while keeping the state probabilities invariant, a learning rule for Boltzmann machines will also find that
distribution in the {0, 1}-coding, which we demonstrate in software simulations. In other words, spiking networks performing neural sampling are
impervious to noise correlations when appropriately trained. This means
that, if such computation happens in cortex, network plasticity does not
need to take particular account of shared noise inputs.
Acknowledgements
Authors Bytschok, Dold and Petrovici contributed equally to this work.
This research was supported by EU grants #269921 (BrainScaleS),
#604102 (Human Brain Project) and the Manfred Stärk Foundation.
BMC Neurosci 2017, 18(Suppl 1):60
References
1. Körding K, Wolpert D: Bayesian integration in sensorimotor learning. Nature
2004
2. Fizser J, Berkes P, Orbán G, Lengyel M: Statistically optimal perception and
learning: from behavior to neural representations. Trends in Cognitive Sciences 2010
3. Rich EL, Wallis JD: Decoding subjective decisions from orbitofrontal cortex.
Nature Neuroscience 2016
4. Buesing L, Bill J, Nessler B, Maass W: Neural dynamics as sampling: a model
for stochastic computation in recurrent networks of spiking neurons. PLoS
Comput Biol 2011
5. Petrovici MA, Bill J, Bytschok I, Schemmel J, Meier K: Stochastic inference
with spiking neurons in the high-conductance state. Physical Review E 2016
6. Furber S: Large-scale neuromorphic computing systems. Journal of Neural
Engineering 2016
P201
Optimal refractoriness from a rate‑distortion perspective
Hui-An Shen, Simone Carlo Surace, Jean-Pascal Pfister
Institute of Neuroinformatics, UZH and ETHZ, Zurich, CH-8057,
Switzerland
Correspondence: Jean-Pascal Pfister (jpfister@ini.uzh.ch)
BMC Neuroscience 2017, 18 (Suppl 1):P201
The information transfer from neuron to neuron through chemical synapses undergoes two stages. In the presynaptic neuron, the
(analog) membrane potential is encoded into a (digital) spike while in
the postsynaptic neuron, this digital information is turned back into an
(analog) depolarisation. It has been argued that for a given inhomogeneous Poisson encoder, the optimal decoder has dynamics that is
consistent with short-term plasticity [1]. However, the optimal encoder
is not known. Here, by studying the rate-distortion performance, we
explore how presynaptic refractoriness influences the performance of
the optimal postsynaptic decoder. First, we generalize the results of
[2] and [3] by expressing the mutual information as a function of the
mean natural estimation loss, in the presence of refractoriness. This
expression provides a numerically stable and fast method of computing mutual information between two high-dimensional random variables. Next, we show with numerical simulations that for a fixed firing
rate ranging from 20-120 Hz, there is an optimal level of refractoriness
that minimizes the distortion, i.e. the mean squared error of the optimal postsynaptic decoder. To test our theory, we compare this optimal
level of refractoriness with an HVC neuron in Zebra Finch to which the
model has been fitted [4].
References
1. Pfister JP, Dayan P, Lengyel M: Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials. Nat Neurosci. 2010,
13(10):1271–1275.
2. Atar R, Weissman T: Mutual information, relative entropy, and estimation in the Poisson channel. IEEE Transactions on Information theory 2012,
58(3):1302–1318.
3. Liptser RS, Shiryaev AN: Statistics of Random Processes II, 2nd Edition. New
York: Springer-Verlag; 2001.
4. Surace SC, Pfister JP: A statistical model for in vivo neuronal dynamics. PloS
one 2015, 10(11):e0142435.
P202
Towards online accurate spike sorting for hundreds of channels
Baptiste Lefebvre,, Marcel Stimberg, Olivier Marre, Pierre Yger
Institut de la Vision, INSERM UMRS 968, CNRS UMR 7210, Paris, France
Correspondence: Pierre Yger (pierre.yger@inserm.com)
BMC Neuroscience 2017, 18 (Suppl 1):P202
Understanding how assemblies of neurons encode information
requires recording of large populations of cells in the brain. In recent
years, multi-electrode arrays and large silicon probes have been developed to record simultaneously from thousands of electrodes packed
with a high density. To tackle the fact that these new devices challenge the classical way to perform spike sorting, we recently developed a fast and accurate spike sorting algorithm (available as an open
Page 118 of 176
source software, called SpyKING CIRCUS), validated both with in vivo
and in vitro ground truth experiments [1]. The software, performing
a smart clustering of the spike waveforms followed by a greedy template-matching reconstruction of the signal, is able to scale to up to
4225 channels in parallel, solving the problem of temporally overlapping spikes. It thus appears as a general solution to sort, offline, spikes
from large-scale extracellular recordings.
In this work, we aim at implementing this algorithm in an “online”
mode, sorting spikes in real time while the data are acquired, to allow
closed-loop experiments for high density electrophysiology. To achieve
such a goal, we built a robust architecture for distributed asynchronous
computations and we propose a modified algorithm that is composed
of two concurrent processes running continuously: 1) “a templatefinding” process to extract the cell templates (i.e. the pattern of activity
evoked over many electrodes when one neuron fires an action potential) over the recent time course; 2) a “template-matching” process
where the templates are matched onto the raw data to identify the
spikes. The main challenge is to have a continuous update of the set
of templates, with hundreds of electrodes and possible drifts over the
time course of the experiment. A key advantage of our implementation
is to be parallelized over a computing cluster to use optimally the computing resources: all the different processing steps of the algorithms
(whitening, filtering, spike detection, template identification and fit)
can be distributed according to the computational needs. During the
clustering, the most computationally demanding step, templates are
detected and tracked over time using a modified version of the density based clustering algorithm [2] able to handle data streams. Our
software is therefore a promising solution for future closed-loop experiments involving recordings with hundreds of electrodes.
References
1. P. Yger et al., Fast and accurate spike sorting in vitro and in vivo for up to
thousands of electrodes, BioRxiv 2016.
2. A. Rodriguez et al., Clustering by fast search and find of density peaks, Science 2014.
P203
Modeling orientation preference in the apical and basal trees
of L2/3 V1
neurons
Athanasia Papoutsi1, Jiyoung Park2, Ryan Ash2, Stelios Smirnakis2,
Panayiota Poirazi1
1
IMBB, FORTH, Heraklion, Crete, 70013, Greece; 2Neurology, Baylor College
of Medicine, Houston, Texas, 77030, USA
Correspondence: Athanasia Papoutsi (athpapoutsi@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P203
Pyramidal neurons receive inputs in two anatomically and functional
distinct domains [1], the apical and the basal tree. Inputs to the basal
tree, due to their proximity to the soma, greatly influence neuronal
output, whereas the more remote apical tree has less potential to
influence somatic activity. How these inputs co-operate to form the
functional output of the neurons is currently unknown. In this work,
we focused on how inputs to the apical and basal trees shape orientation tuning in L2/3 V1 neurons. In particular, we investigated how
dendritic integration of orientation tuned inputs to the apical versus
basal trees allows for the emergence of stable neuronal orientation
preference. Towards this goal, a model L2/3 V1 pyramidal neuron was
implemented in the NEURON simulation environment. The passive
and active properties of the model neuron were extensively validated
against experimental data. Synaptic properties, number and distribution were also constrained according to available data (Figure 1A).
Using this model neuron, we investigated a) the differences in the
mean orientation preferences of the two trees and b) the distribution of orientation preferences to individual synapses that allow for
the emergence of orientation tuning (Figure 1B). Given the parameter
combinations that allow for the emergence of orientation tuning (Figure 1C), we found that neuronal orientation tuning follows in large
part the orientation tuning of the basal tree. In addition, we have further identified how apical versus basal dendritic tree ablation would
affect neuronal tuning in the different conditions implemented. Model
BMC Neurosci 2017, 18(Suppl 1):60
results provide insights regarding the ‘tolerance’ to different input
properties at the apical and basal tree in order to achieve stable orientation preference.
Figure 1. A. Top: From the pool of synapses, 25% were stimulus
driven (black dots). Bottom: Indicative trace showing fluctuations
of the membrane potential in the presence of background synaptic activity. Spikes are truncated for visualization purposes. B. Each
tree was characterized by a μ ± σ orientation preference that was
determined by the preferences of the individual synapses. Here it
is shown the portion of synapses with same/different orientation
preferences from the μtree, for σtree = 3, 15, 30, 45 and 60°. Grouping to the reported value (x-axis) includes ± 10° differences. C. Orientation tuning curve of the model neuron (mean ff ± sem). Right:
Indicative voltage traces of the neuronal responses for different
bar orientations (0°, 30°, 60° and 90°)
Reference
1. Larkum ME: A cellular mechanism for cortical associations: an organizing
principle for the cerebral cortex. Trends Neurosci 2012:1–11.
P204
Dual recordings in the mouse auditory brainstem and midbrain
reveal differences in the processing of vocalizations
Richard A. Felix1, Alexander G. Dimitrov1,2, Christine Portfors1
1
Department of Integrative Biology and Neuroscience, Washington
State University Vancouver, Vancouver WA 98686, USA; 2Department
of Mathematics and Statistics, Washington State University Vancouver,
Vancouver WA 98686, USA
Correspondence: Alexander G. Dimitrov (alex.dimitrov@wsu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P204
Background: A normal functioning auditory system must rely on
fast and precise neuronal responses in order to accurately represent
temporal information in complex sounds. Impairments in temporal
processing contribute to a variety of listening disorders, yet our understanding of mechanisms that govern these processes remains limited.
We examined how enhanced spike timing at the level of the inferior
colliculus (IC) in the midbrain might underlie efficient encoding of
vocalizations compared to the cochlear nucleus (CN), an earlier site in
the ascending auditory pathway.
Methods: We recorded neuronal responses to conspecific vocalizations in the IC and CN of awake, normal-hearing mice that expressed
Channelrhodopsin in VGlut2-positive neurons. We used an optrode
that combined the recording of single unit activity with light delivery to the CN. Once a recording was established in the CN, a second
electrode was placed in the IC and dual recordings were established
at locations with matching frequency tuning. The CN was stimulated
with light in the absence of sound to measure effects in the IC and
then responses to sound stimuli were simultaneously recorded at each
site. We assessed the extent of functional connectivity between CN
Page 119 of 176
and IC recording sites, the temporal precision of evoked spiking, and
the neuronal selectivity to vocalization stimuli, using statistical and
information-theoretic tools.
Results: We found that stimulating the CN with light caused evoked
activity in the IC when the two recording sites had matched frequency
tuning, suggesting that tonotopic organization reliably predicts functional connectivity between the sites. Despite matching frequency
tuning, IC neurons exhibited greater selectivity to a common set of
vocalization stimuli compared to the dorsal CN (DCN). Overall, CN
responses had higher rates of evoked spiking, while IC responses were
more transient and had enhanced spike timing, suggesting a shift
toward the extraction of temporal information contained in vocalizations at the level of the midbrain (Figure 1).
Figure 1. Relationship between information content and response
consistency in mouse DCN and IC
Conclusion: Neurons in the CN often contributed to activity recorded
in the IC. Dual recordings conducted under the same experimental
conditions that have a degree of functional connectivity provide a
strong paradigm for comparing processing at different stages of the
auditory pathway. Enhanced selectivity to vocalizations and temporal precision of responses in the IC suggests that this region may be
important for encoding biologically important sounds. When auditory
processing is impaired, the IC may be a subcortical site for the generation of auditory disorders typically thought to arise in the cortex.
P205
Modelling of leg decoupling in the stick insect and its possible
significance for understanding the workings of the locomotor
system
Silvia Daun1,2, Tibor I. Toth1
1
Department of Animal Physiology, Institute of Zoology, University
of Cologne, Cologne, 50674, Germany; 2Cognitive Neuroscience, Institute
of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich,
52425, Germany
Correspondence: Silvia Daun (Silvia.Daun@uni-koeln.de)
BMC Neuroscience 2017, 18 (Suppl 1):P205
Amputation and temporary restraint of legs are widely used and accepted
methods of the study of the locomotor systems of insects. The animal
is studied during free walking, and its walking behaviour is compared
before and after the amputation. Using the results, conclusions are drawn
with regard to the organization of the locomotor system of the animal in
question. In the stick insect, such investigations were carried out by [1]
and more recently by [2]. In the latter study, it was even observed that
the front legs could reversibly be decoupled by the animal itself and used
to carry out search movements. Nevertheless, the hind and middle legs
continued their coordinated walking. From these and other experimental
BMC Neurosci 2017, 18(Suppl 1):60
observations detailed in [1] and [2], the question naturally arising is: what
mechanisms underlie the changes found in the experiments. The underlying mechanisms obviously belong to the part of the nervous system that
controls and coordinates locomotion. One promising way to study them is
by using appropriate mathematical models. We used an existing model of
coordinated stepping of the three ipsilateral legs of the stick insect [3] to
mimic the various decoupling situations in the stick insect described in [1]
and [2]. In the model, the levator-depressor neuro-muscular control networks (LD systems) of the individual legs play a pivotal role in producing
coordinated stepping of the legs. We identified three main possibilities of
decoupling a single leg: i) disrupting the inter-leg coordination between
the legs’ LD systems; ii) blocking the normal function of the central pattern
generator of the LD system of the leg to be decoupled; and iii) changing
the activity of the levator and depressor motoneurones via their associated pre-motor inhibitory interneurones. Decoupling of the front leg in
the model worked with any of the methods i)-iii). It was easily reversible,
in accordance with the observations that such reversible decoupling happens in natural conditions when the animal uses its front legs for searching. The hind and middle leg continued their coordinated stepping, like
in the experiments [1, 2]. Decoupling of the hind leg was most effective
when method iii) was used. In this case, the middle and the front leg continued performing coordinated stepping irrespective of the decoupling
method, in agreement with the experimental findings. In the model, the
middle leg took over automatically the role of the hind leg as the origin
of the coordinated stepping. Decoupling the middle leg yielded mixed
results: in some cases, depending on the phase within a stepping period,
the coordinated stepping of the front and hind leg was abolished, in others, it was not but its quantitative properties were changed. Both types of
results were also found in the experiments [1, 2].
In conclusion, we suggest that, depending on the leg, various mechanisms are possible to decouple it from the system of inter-leg coordination. In all cases, method iii) worked most reliably and efficiently.
However, the other mechanisms (methods) may represent redundance and can be activated, if necessary, to bring about decoupling
of the leg.
Acknowledgements
This work was supported by the DFG grants to S. Daun (GR3690/2-1
and GR3690/4-1).
References
1. Graham D: The effect of amputation and leg restraint on the free walking
coordination of the stick insect Carausius Morosus. J Comp Physiol 1977,
116:91–116.
2. Grabowska M, Godlewska E, Schmidt J, Daun-Gruhn S: Quadrupedal gaits
in hexapod animals - inter-leg coordination in free walking adult stick
insects. J Exp Biol 2012, 215:4255–4266.
3. Toth TI, Daun-Gruhn S: A three-leg model producing tetrapod and tripod
coordination patterns of ipsilateral legs in the stick insect. J Neurophysiol
2016, 115:887–906.
P206
Spatio‑temporal dynamics of key signaling molecules in growth
cones
Joanna Jędrzejewska-Szmek1, Nadine Kabbani1,2, Kim T. Blackwel1,3
1
Krasnow Institute, George Mason University, Fairfax, VA 22030, USA;
2
School of Systems Biology, George Mason University, Fairfax, VA 22030,
USA; 3Bioengineering Department, George Mason University, Fairfax, VA
22030, USA
Correspondence: Joanna Jędrzejewska-Szmek (jjedrzej@gmu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P206
Growth cones, guided by environmental cues, are necessary for proper
neural functioning. The cues are detected by membrane-bound receptors, which in turn activate a plethora of signaling pathways. A majority of these pathways is governed by calcium, flowing into the growth
cone through the plasmalemma or from the calcium stores. Both the
magnitude of calcium increase and identity of calcium source seem
to determine neural growth and retraction [1]. Calcium exerts its control through a variety of signaling molecules that interact non-linearly.
This picture is further complicated by recent findings showing that the
Page 120 of 176
ionotropic alpha7 nicotinic receptor (a7nAChR) also has a metabotropic
function and couples to heteromeric Gq proteins. A7nAChR action via
the Gq pathway results in calcium release from the endoplasmic reticulum (ER) modulating cytoskeletal motility and structural growth [2–4].
Experimental evidence shows that both low and high cytosolic calcium results in growth cone repulsion, and medium cytosolic calcium
results in attraction. It also shows that calcium influx through the plasmalemma results in repulsion and calcium influx from the internal
stores results in growth. To investigate and unify these seemingly contradictory observations experimental observations, we developed a
stochastic reaction-diffusion model of calcium, cAMP and Gq activated
pathways. The model allows for evaluating the role of the transient
calcium influx through the channel pore (the ionotropic contribution)
compared to the role of calcium release caused by activation of the
Gq subtype of GTP binding protein. Using the model, we investigated
whether combined metabotropic and ionotropic action of a7nAChR,
resulting in prolonged increase of cytosolic calcium, is responsible for
experimentally observed growth attenuation.
To test whether we can predict neurite outgrowth and retraction in
response to various environmental stimuli and to elucidate contribution of molecular gradients we looked at combined action of key signaling molecules. We show that combined activation of calcium and
cAMP activated targets such as PP2B and PP1, CaMKII, PKA and calpain can explain the non-monotonic dependence of structural growth
on calcium levels. Elucidating the mechanisms underlying synaptic
growth will allow for better understanding of mechanisms of neural
development and regeneration
Acknowledgements
The joint NIH-NSF CRCNS program through NSF grant 1515686
References
1. Henley J, Poo M-m: Guiding neuronal growth cones using Ca2+ signals.
Trends in Cell Biol 2004, 14:320–330. doi: 10.1016/j.tcb.2004.04.006
2. Nordman JC, Kabbani N: Microtubule dynamics at the growth cone are
mediated by α7 nicotinic receptor activation of a Gαq and IP3 receptor
pathway. FASEB J 2014, 28:2995–3006. doi: 10.1096/fj.14-251439.
3. King JR, Nordman JC, Bridges SP, Lin MK, Kabbani N. Identification and
characterization of a G protein-binding cluster in α7 nicotinic acetylcholine
receptors. J Biol Chem 2015, 290:20060–70. doi:10.1074/jbc.M115.647040
4. King JR, Kabbani N: Alpha 7 nicotinic receptor coupling to heterotrimeric
G proteins modulates RhoA activation, cytoskeletal motility, and structural
growth. J Neurochem 2016, 138:532–45. doi:10.1111/jnc.13660.
P207
A simulation of EMG signal generation following TMS
Bahar Moezzi1,2, Natalie Schaworonkow3, Lukas Plogmacher3, Mitchell
R. Goldsworthy2,4, Brenton Hordacre2, Mark D. McDonnell1, Nicolangelo
Iannella1,5, Michael C. Ridding2, Jochen Triesch3
1
Computational and Theoretical Neuroscience Laboratory, School
of Information Technology and Mathematical Sciences, University
of South Australia, Adelaide, Australia; 2Robinson Research Institute,
School of Medicine, University of Adelaide, Adelaide, Australia; 3Frankfurt
Institute for Advanced Studies, Frankfurt, Germany; 4Discipline
of Psychiatry, School of Medicine, University of Adelaide, Adelaide,
Australia; 5School of Mathematical Sciences, University of Nottingham,
Nottingham, UK
Correspondence: Bahar Moezzi (bahar.moezzi@mymail.unisa.edu.au)
BMC Neuroscience 2017, 18 (Suppl 1):P207
Transcranial magnetic stimulation (TMS) is a technique that allows
noninvasive manipulation of neural activity and is used extensively in
both clinical and basic research settings [1]. The effect of TMS on motor
cortex is often measured by electromyography (EMG) recordings from
a small hand muscle, such as the first dorsal interosseous (FDI). However, the details of how TMS generates responses measured with EMG
are not completely understood. Here, we aim to develop a biophysically detailed computational model to study the potential mechanisms
underlying the generation of EMG signals in response to TMS.
Our model comprises a feed-forward network of cortical layer 2/3 cells,
which drive morphologically detailed layer 5 corticomotoneuronal
BMC Neurosci 2017, 18(Suppl 1):60
cells based on [2]. The cortical layer 5 cells in turn project to a pool of
motoneurons and eventually the muscle. The EMG signal is the sum of
motor unit action potentials. Model parameters are tuned to match
results from EMG recordings from the FDI muscle performed in four
human subjects.
The model successfully reproduces several properties of the experimental data. The simulated EMG signals match experimental EMG
recordings in shape and size, and vary with stimulus and contraction
intensities as in experimental data. They exhibit cortical silent periods that are close to the biological values, and reveal an interesting
dependence on inhibitory synaptic transmission characteristics. Our
model predicts neural firing patterns along the entire pathway from
cortical layer 2/3 cells down to spinal motoneurons. In conclusion, our
model successfully reproduces major features of EMG recordings and
should be considered as a viable tool for analyzing and explaining
EMG signals following TMS.
References
1. Hallett M: Transcranial magnetic stimulation and the human brain. Nature
2000, 406:147–150.
2. Rusu CV, Murakami M, Ziemann U, Triesch J. A model of TMS-induced
I-waves in motor cortex. Brain Stimul 2014, 7:401–414.
P208
The effect of LTP, LTD and non‑specific LTD on the Recognition
of Sparse Noisy Patterns in Simplified and Detailed Purkinje Cell
Models
Reinoud Maex1, Karen Safaryan2, Volker Steuber3
1
Department of Cognitive Sciences, Ecole Normale Supérieure, rue
d’Ulm 25, 75005 Paris, France; 2Department of Physics and Astronomy,
Knudsen Hall, University of California, Los Angeles, CA, 90095-0001,
USA; 3Centre for Computer Science and Informatics Research, University
of Hertfordshire, College Lane, Hatfield, AL10 9AB, United Kingdom
Correspondence: Reinoud Maex (reinoud.maex@ens.fr)
BMC Neuroscience 2017, 18 (Suppl 1):P208
Classic theories of cerebellar learning suggest that parallel fibre (PF)
activity patterns in cerebellar cortex can be stored and recalled based
on long-term depression (LTD) of PF - Purkinje cell synapses [1, 2]. As in
other theories of learning in neural systems, it is commonly assumed
that the weight changes are limited to activated synapses. However,
it has been shown that a non-specific form of PF LTD can spread to
neighbouring synapses that are inactive during learning [3]. Moreover,
long-term potentiation (LTP) of PF synapses has also been found to
contribute to cerebellar learning [4].
We have previously studied the effect of non-specific LTD (nsLTD) on
pattern recognition and have shown that nsLTD can provide robustness against local spatial noise in the input patterns [5]. Here we
extend our previous work by studying the functional role of LTP, and
we investigate other determinants of the pattern recognition performance such as the sparsity and number of patterns and different
types of pattern noise. We compare results from numerical simulations of a morphologically realistic conductance based Purkinje cell
model (as in [2]) with those of a simple linear artificial neural network
(ANN) unit. Further, to better understand the results of the numerical simulations, we perform a mathematical analysis of the pattern
recognition performance of the ANN unit. As in previous work, we
quantify the pattern recognition performance by calculating a signalto-noise (s/n) ratio [2, 5].
The simulations and analysis of the ANN unit predict that adding LTP
to the learning rule does not affect the pattern recognition performance, given that the mean and variance of responses, which appear
in the enumerator and denominator of the s/n ratio, respectively, are
equally affected by LTP. In contrast, however, the pattern recognition
performance of the Purkinje cell model was sensitive to the average
synaptic weight, which determined both the spontaneous spike rate
and the response to pattern presentation. Adding LTP in the Purkinje
cell model made nsLTD equivalent or superior to LTD at all noise levels. Moreover, the LTP based normalisation of weights prevented the
Purkinje cell responses from becoming too weak and increased the
number of patterns that could be stored for a given s/n ratio by a
Page 121 of 176
factor of 4. Finally, we show that our previous conclusions hold over a
large range of pattern loadings and sparsities, and that local additive
pattern noise can further increase the beneficial effect of nsLTD.
References
1. Marr D: A theory of cerebellar cortex. J Physiol 1969, 202:437–470.
2. Steuber V, Mittmann W, Hoebeek FE, Silver RA, De Zeeuw CI, Hausser M, De
Schutter E: Cerebellar LTD and pattern recognition by Purkinje cells. Neuron
2007, 54:121–136.
3. Wang SS, Khiroug L, Augustine GJ: Quantification of spread of cerebellar
long-term depression with chemical two-photon uncaging of glutamate.
Proc Natl Acad Sci USA 2000, 97:8635–8640.
4. Schonewille M, Belmeguenai A, Koekkoek SK, Houtman SH, Boele HJ, van
Beugen BJ, Gao Z, Badura A, Ohtsuki G, Amerika WE, Hosy E, Hoebeek FE,
Elgersma Y, Hansel C, De Zeeuw CI: Purkinje cell-specific knockout of the
protein phosphatase PP2B impairs potentiation and cerebellar motor
learning. Neuron 2010, 67:618–628.
5. Safaryan K, Maex R, Adams RG, Davey N, Steuber V: Non-specific LTD at parallel fibre - Purkinje cell synapses in cerebellar cortex provides robustness
against local spatial noise during pattern recognition. BMC Neuroscience
2011, 12:P314.
P209
Modeling causality of the smoking brain
Rongxiang Tang1, Yi-Yuan Tang2
1
Department of Psychology, Washington University in St. Louis, St. Louis,
MO 63130, USA; 2Department of Psychological Sciences, Texas Tech
University, TX 79409, USA
Correspondence: Yi-Yuan Tang (yiyuan.tang@ttu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P209
Previous studies indicated that brain areas including prefrontal cortex
(e.g., medial prefrontal cortex, mPFC), posterior cingulate cortex (PCC)
and insula involved in smoking addiction [1]. However, functional connectivity among these regions only shows the correlative relationship
but does not reveal the causal relationship such as the changes in
information flow in these distributed brain areas involved in smoking.
In prior studies [2-3], we applied a newly developed spectral dynamic
causal modeling (spDCM) to resting state fMRI to demonstrate the
causal relationships among the core regions in smoking addiction. Our
results suggested that compared to nonsmokers, smokers had reduced
effective connectivity from PCC to mPFC and from right inferior parietal
lobule (R-IPL) to mPFC, a higher self-inhibition within PCC and a reduction in the amplitude of endogenous neuronal fluctuations driving the
mPFC [2]. Given that Granger causality (GC) and DCM are two main causality methods and have distinct but complementary ambitions that
are usefully considered in relation to the detection of functional connectivity and the identification of models of effective connectivity [4-5],
therefore it’s important to use a same dataset to compare two models.
We used the dataset of college students previously reported in our
study [2]. All fMRI data were collected using a 3-Telsa Siemens Skyra
scanner and processed using the Data Processing Assistant for Resting-State fMRI, which is based on SPM and Resting-State fMRI Data
Analysis Toolkit [2-3]. For fMRI analyses, we conducted the standard
procedures included slice timing, motion correction, regression of
WM/CSF signals and spatial normalization [3]. A standard GC analysis
was also applied to test the causality among key regions involved in
smoking [5-6]. Based on previous literature, in this study we specified
four regions of interest within default mode network (DMN) - medial
prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and bilateral inferior parietal lobule (Left IPL and Right IPL), same coordinates
as in previous spDCM studies [2]. Our results showed the similar causal
relationship among these brain areas.
Conclusions: GC and DCM are complementary: both are concerned
with directed causal interactions. GC models dependency among
observed responses, while DCM models coupling among the hidden
states generating observations. Despite this fundamental difference,
the two approaches may be converging.
Acknowledgements
This work was supported by the Office of Naval Research.
BMC Neurosci 2017, 18(Suppl 1):60
References
1. Goldstein RZ, Volkow ND: Dysfunction of the prefrontal cortex in addiction: Neuroimaging findings and clinical implications. Nat Rev Neurosci 2011, 12:652–669.
2. Tang R, Razi A, Friston KJ, Tang YY: Mapping smoking addiction using effective connectivity analysis. Frontiers in Human Neuroscience. 2016, 10:195.
3. Razi A, Kahan J, Rees G, Friston KJ: Construct validation of a DCM for resting
state fMRI. Neuroimage 2015, 106:1–14.
4. Friston K, Moran R, Seth AK: Analysing connectivity with Granger causality
and dynamic causal modelling. Curr Opin Neurobiol. 2013, 23:172–8.
5. Seth AK: A MATLAB toolbox for Granger causal connectivity analysis. J
Neurosci Meth 2010, 186:262–273.
6. Zhao Z, Wang X, Fan M, Yin D, Sun L, Jia J, Tang C, Zheng X, Jiang Y, Wu J, Gong J:
Altered effective connectivity of the primary motor cortex in stroke: a restingstate fmri study with Granger causality analysis. PLoS One. 2016, 11:e0166210.
P210
Modelling of calcium waves in astrocytic networks induced
by neural activity
Darya V. Verveyko1, Alexey R. Brazhe2, Andrey Yu Verisokin1, Dmitry E.
Postnov3
1
Department of Theoretical Physics, Kursk State University, Kursk, 305000,
Russian Federation; 2Department of Biophysics, Lomonosov Moscow State
University, Moscow, 119991, Russian Federation; 3Department of Physics,
Saratov State National Research University, Saratov, 410012, Russian Federation
Correspondence: Darya V. Verveyko (allegroform@mail.ru)
BMC Neuroscience 2017, 18 (Suppl 1):P210
We propose two-compartment model of calcium dynamics in astrocyte network, basing on Ullah model [1]. In order to count the specific
features of different parts of astrocyte network we mark out three
types of modelling space: astrocyte soma with thick branches, thin
branches, and extracellular space. We have developed two variants
of equation set which are different in relative contribution of specific
ionic currents. We suppose that activation of astrocyte calcium dynamics is mediated by the extracellular space, specifically, via diffusion of
synaptic glutamate released due to the neuronal activity, which we
describe as some random signal incorporating noise effects.
We have performed a number of simulation runs at different parameter
sets for individual astrocyte and multi-cell network. One of simulation
examples within the computational multi-cell template is given in Figure 1. The global wave emerging in one of the points passes through
the wide region of astrocyte network. The formation of the wave has a
high degree of regularity and periodicity. There are also local regimes
where excitation waves damp passing through a small number of cells.
Figure 1. Calcium global wave in multi-cell ensemble. A. The
representative snapshots of spatial patterns. Numbers from 1 to
8 indicate the cells according to its involvement in firing pattern.
B, C. The time courses of cytosolic Ca2+ and IP3 concentrations,
respectively
Page 122 of 176
Conclusions: We have suggested the advanced model of astrocyte
network dynamics, which fits well the recent experimental findings [2].
Specifically, we have suggested the development of model equations
for intra-astrocyte calcium dynamics, which takes into account its specific topological features. We have tested the suggested approach for
both individual cell image and multi-cellular structure. The obtained
results confirm that our model is able to reproduce the evolution of
spatio-temporal dynamics under neuronal activity represented by
spatially uncorrelated and randomized in time process of glutamate
injection. In multicellular system, a persistent self-organized rhythmicity of calcium activity in groups was found which can be explained by
some interplay between the refractory time of calcium excitability and
noise-triggered processes.
Acknowledgements
This work is partially supported by the Ministry of Education and
Science of the Russian Federation within the research project
№3.9499.2017 included into the basic part of research funding
assigned to Kursk State University.
References
1. G. Ullah, P. Jung, A.H. Cornell-Bell: Anti-phase calcium oscillations in astrocytes via inositol (1, 4, 5)-trisphosphate regeneration. Cell Calcium 2006, 39:
197–208.
2. M. Falcke: Reading the patterns in living cells - the Physics of Ca2+ signaling. Adv. in Phys. 2004, 53(3): 255–44
P211
Simulated voltage clamp: offline biophysical reconstruction of fast
ionic currents in large cells with uncompensated series resistance
Cengiz Günay1,2, Gabriella Panuccio3, Michele Giugliano3, Astrid A. Prinz1
1
Dept. Biology, Emory University, Atlanta, Georgia 30322, USA; 2School
of Science and Technology, Georgia Gwinnett College, Lawrenceville,
Georgia 30043, USA; 3Theoretical Neurobiology & Neuroengineering Lab,
Dept. Biomedical Sciences, University of Antwerp, Antwerp, Belgium
Correspondence: Cengiz Günay (cgunay@ggc.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P211
Characterization of ion channel kinetics from voltage-clamp experiments is inherently biased by the non-linear voltage error introduced
by the resistance of the recording pipette in series with the membrane resistance (series resistance, Rs) [1]. Modern patch-clamp amplifiers provide built-in circuits for on-line Rs compensation. However,
because of the nature of these circuits, it is theoretically impossible to
achieve 100% Rs compensation without losing stability of the recording. Moreover, fast ionic voltage-dependent currents, like sodium
(Na+) currents, require a high band-width operation of the Rs compensation circuit, which in turn might result in sudden oscillations of
the cell membrane voltage (Vm). Consequently, Rs compensation is
currently a trade-off between a commonly accepted error tolerance
and the crucial need for preventing oscillations. Here, we build a novel
“simulation method” as a new component to a previously developed
computational framework [2] to overcome these limitations. In contrast to the amplifier’s strategy to force a flat voltage waveform, which
is required for generating conventional current-voltage plots of peak
ionic currents, we allow arbitrary voltage waveforms by simulating
voltage-clamp in a computational neuron model and then curve fitting its output to match recordings to directly estimate HodgkinHuxley model parameters of the channel. The kinetics parameters
so obtained are used to reconstruct the unbiased current trace. We
demonstrate our method using voltage-clamp recordings of Na+
currents from ‘giant’ layer V pyramidal cells of the rat primary somatosensory cortex in the presence of uncompensated, significantly high
(10-20 MΩ) Rs along with the low input resistance (~40 MΩ) typical of
these cells, so as to maximize the compound voltage clamp errors. As
shown in Figure 1, the model computes non-linear artifact currents
and predicted actual Vm values. When Rs compensation is a major
concern for the reliability voltage-clamp data, our approach is capable
of overcoming the limitations posed by currently available hardwareand software-based Rs compensation methods, thus allowing to fully
reconstructing the actual current kinetics.
BMC Neurosci 2017, 18(Suppl 1):60
Figure 1. Offline subtraction of estimated amplifier-unaccounted
passive currents. A. Raw recordings of Na+ currents contaminated
by uncompensated artifacts (top) recorded during the corresponding voltage steps (bottom trace). B. Passive artifacts subtracted
from the current traces (top) and actual Vm (bottom) estimated
using the model simulation method. Note how the actual Vm differs significantly from the desired holding voltage-steps (see panel
A)
Acknowledgements
Career Award at the Scientific Interface (CASI) from the Burroughs
Wellcome Fund awarded to AAP.
References
1. Sakmann, B., and Neher, E. Single-Channel Recording. 2nd Edition, (Springer
Science & Business Media, Plenum Press, New York, 1995).
2. Günay C, Edgerton JR, Li S, Sangrey T, Prinz AA, and Jaeger D. Database
analysis of simulated and recorded electrophysiological datasets with
PANDORA’s toolbox. Neuroinformatics 2009, 7: 93–111.
P212
Representing and implementing cognitive sequential interactions
Pablo Varona1, Mikhail I. Rabinovich2
1
Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática,
Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid,
Spain; 2BioCircuits Institute, University of California, San Diego, CA, USA
Correspondence: Pablo Varona (pablo.varona@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P212
Cognition as observed by imaging experiments involves sequential
activations of different brain regions [1]. The sequential nature of most
aspects of cognition is also reflected in the progression of successive
components of decision-making and behavior. In this work, we present a family of models that describe hierarchical relationships among
cognitive processes represented with robust sequential dynamics.
These models build heteroclinic networks based on the winnerless
competition principle where asymmetric inhibition shapes key properties for sequential information processing. The robustness of the
sequential dynamics in these networks relies on stable heteroclinic
channels, sequences of metastable states and their vicinity connected
by separatrices that link them in a chain.
The models described in this work are implemented with generalized Lotka-Volterra equations whose variables can represent information perception items and also cognitive resources such as attention,
working-memory and emotion [2–5]. Their hierarchical interactions
give rise to binding and chunking processes. We discuss applications
of these models in three different contexts: (i) the characterization
of decision-making in terms of the sequential evolution of incoming
information and the hierarchical organization of cognitive resources
in time; (ii) the use of these models to build joint robot-human interactions which result in an increased joint creativity of such team; (iii)
the use of these models to drive closed-loop stimulation in novel
experiments to reveal healthy and pathological dynamics of cognitive
processes in normal subjects and in subjects with cognitive impairments. The considered dissipative models are in general structurally
stable and suitable for bifurcation analysis, which helps their interpretation in relationship with experimental data. Their robustness
Page 123 of 176
and computational efficiency also make them adequate for real-time
implementations in the proposed applications.
Overall, we stress the need to interpret brain imaging experiments
in the context of theoretical studies that describe information flows
corresponding to sequential cognitive processes. The coarse-grained
information of current imaging techniques can be matched to the
variables represented in the proposed network models. The results
of such analyses can lead to novel insights linking networks graphs
to cognitive dynamics, and the development of novel technology for
rehabilitation purposes and artificial cognition.
Acknowledgements
This work was funded by MINECO/FEDER DPI2015-65833-P (http://
www.mineco.gob.es/) and ONRG grant N62909-14-1-N279 (PV) and by
ONR MURI 14-13-1-0205 and MURI N00014-13-1-0678 (MIR)
References
1. Daselaar SM, Rice HJ, Greenberg DL, Cabeza R, LaBar KS, Rubin DC. The
spatiotemporal dynamics of autobiographical memory: Neural correlates
of recall, emotional intensity, and reliving. Cereb. Cortex. 2008; 18:217–29.
2. Rabinovich MI, Afraimovich VS, Bick C, Varona P. Information flow dynamics
in the brain. Phys. Life Rev. 2012; 9:51–73.
3. Rabinovich MI, Tristan I, Varona P. Hierarchical nonlinear dynamics of
human attention. Neurosci. Biobehav. Rev. 2015; 55:18–35.
4. Rabinovich MI, Simmons AN, Varona P. Dynamical bridge between brain
and main. Trends Cogn. Sci. 2015; 19:453–461.
5. Varona P, Rabinovich MI. Hierarchical dynamics of informational patterns
and decision making. Proc. R. Soc. B. 2016; 283:20160475.
P213
An integrated neuro‑mechanical model of C. elegans locomotion
Jack Denham, Thomas Ranner, Netta Cohen
School of Computing, University of Leeds, Leeds, LS2 9JT, UK
Correspondence: Jack Denham (scjde@leeds.ac.uk), Thomas Ranner (T.
Ranner@leeds.ac.uk), Netta Cohen (N.Cohen@leeds.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P213
Across the animal kingdom, the generation and modulation of motor
behaviour is attributed to Central Pattern Generators (CPGs) or neural circuits that endogenously produce oscillations. The ubiquity of
CPGs prompts the use of coupled oscillator models to describe neural activity and the generation of behaviour. However, CPGs have
not been identified in the forward locomotion system of the small
roundworm Caenorhabditis elegans. In this case, a proprioceptive
mechanism, in which motor-neurons respond to local body stretch,
is thought to drive sustained body undulations. Since the wavelength
and frequency of oscillations has been shown to depend on the viscoelasticity of the surrounding medium [1], it is important to include
environmental effects in such locomotion models [1, 2]. This requires
the integration of the nervous system and body mechanics in a continuous feedback loop which is able to adapt in response to environmental changes. Here, a biologically grounded model describing neural
activity (adapted from [1]) is integrated into a novel continuum softbody model [2]. We present a dynamical systems description of the
local pattern generation mechanism with fictive proprioceptive feedback and compare this with the actual feedback in whole body simulations. The closed loop neuro-mechanical model is demonstrated to
produce realistic travelling waves down the body in silico. The effect of
the material properties of the body is investigated.
References
1. Boyle JH, Berri S, Cohen N: Gait modulation in c. elegans: an integrated
neuro-mechanical model. Frontiers in computational neuroscience 2012,
6:10.
2. Cohen N, Ranner T: A new computational method for a model of C. elegans biomechanics: Insights into elasticity and locomotion performance,
arXiv:1702.04988, 2017.
BMC Neurosci 2017, 18(Suppl 1):60
P214
A computational approach to understanding functional synaptic
diversity: the role of nanoscale topography of Ca2+ channels
and synaptic vesicles
Maria Reva1, Nelson Rebola1, Tekla Kirizs2, Zoltan Nusser2, David
DiGregorio1
1
Laboratory of Dynamic Neuronal Imaging, Neuroscience Department,
Institute Pasteur, Paris, France, 75015; 2Institute of Experimental Medicine,
Hungarian Academy of Sciences, Budapest, Hungary, 1083
Correspondence: Maria Reva (maria.reva@pasteur.fr)
BMC Neuroscience 2017, 18 (Suppl 1):P214
Understanding the spatial relationship between the synaptic vesicles and the voltage-gated Ca2+ channels (VGCCs) is critical for
deciphering the determinants of synaptic strength, time course,
and plasticity. Furthermore, synaptic strength, within a homogeneous population of synapses, is highly heterogeneous, but the
underlying mechanisms are poorly understood. We hypothesize
that variations in the nanoscale organization of VGCCs and synaptic
vesicles contribute to the diversity of synaptic function observed
throughout the brain [1]. Because VGCCs and synaptic vesicles can
be as close as 10-20 nm, direct experimental observation of the
spatio-temporal dynamics driving synaptic vesicle fusion is still
challenging. We have taken a computational approach to simulate
the spatio-temporal dynamics of Ca2+ -triggered vesicle fusion to
examine channel-vesicle topologies that is consistent with experimental findings.
To understand the influence of topography on synaptic diversity, we
performed Monte Carlo (MC) simulations designed to predict the
different functional behavior of inhibitory and excitatory terminals
within the cerebellar cortex. Model parameters were constrained to
experimental data (such as single channel open probability, Ca2+
buffers kinetics, etc.) leaving only topographical arrangements of
VGCCs and location of the release sensor as variables. In addition, we
have analyzed replicas in which the VGCC subunit Cav2.1 was labeled.
Using Ripley’s analysis and mean nearest neighbor distances (NND)
calculations we concluded that the distribution of the Cav2.1 subunit
was significantly different from complete spatial randomness in both
excitatory and inhibitory axon terminals. Then using cluster analysis, we determined that inhibitory terminals exhibited small clusters,
while the labeling on excitatory boutons seemed more amorphous.
We therefore considered an arrangement based on a few simple rules:
VGCCs and vesicles were placed randomly within the AZ, but with
a minimal separation, we called this the exclusion zone (EZ) model.
The EZ model produced channel NND distributions that were consistent with the electron microscopy data. We then performed reaction
diffusion MC simulations, considering perimeter coupled model for
inhibitory terminals and the exclusion topology for excitatory terminals. Our simulations predicted well the experimental data of Ca2+
chelator inhibition of synaptic release (EGTA inhibition) and release
probability.
Our results suggest that inhibitory terminals use small clusters
of VGCC to drive the fusion of vesicles located in their periphery
(perimeter release model) as described previously at the excitatory
calyx of Held synapses [2]. In contrast, excitatory synapses made by
cerebellar parallel fibers require a more random placement of up
to 3 times more VGCCs within the AZ, as well as random placement
of vesicles with an exclusion zone of >40 nm. We therefore suggest
that nanoscale distribution of VGCCs and synaptic vesicles differs
among synapses and is a key factor underlying functional synaptic
diversity.
References
1. Chabrol FP, Arenz A, Wiechert MT, Margrie TW, DiGregorio DA: Synaptic
diversity enables temporal coding of coincident multisensory inputs in
single neurons. Nat Neurosci 2015, 18(5): 718–727.
2. Nakamura Y, Harada H, Kamasawa N, Matsui K, Rothman JS, Shigemoto R,
Silver RA, DiGregorio DA, Takahashi T: Nanoscale distribution of presynaptic
Ca(2 +) channels and its impact on vesicular release during development.
Neuron 2015, 85(1): 145–158.
Page 124 of 176
P215
Is object saliency perceived different cross‑culturally: a
computational modelling study
Eirini Mavritsaki1,2, Panos Rentzelas1
1
Department of Psychology, Birmingham City University, Birmingham, UK;
2
School of Psychology, University of Birmingham, Birmingham, UK
Correspondence: Eirini Mavritsaki (eirini.mavritsaki@bcu.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P215
Research on cross-cultural differences of visual attention has identified
that cultural membership influence performance in object perception
[1, 2]. Participants with collectivist background focus more on the background (distractors) and omit the target relevant information while participants from the individualists’ background tend to attend the target
and omit the background information. Previous modelling work from our
lab [3] predicted that in Visual Search task cultural memberships influences the performance of the tasks. The results showed that simulated
efficiency of participants from the individualist group is significantly
higher than simulated efficiency from participants from the collectivists
group when the task is to identify a target amongst distractors in a classical easy visual search. Work in our lab then confirmed these predictions.
Preliminary behavioral data supports the idea that the effect remains
even if the target is more salient than the distractors. This difference is
simulated and explored further by investigating the changes in the effect
for different levels of saliency using the binding Search over Time and
Space (bsSoTS) computational model [4, 5] as predictor of behavior.
bsSoTS is based on integrate-and-fire neurons that are tighter connected when they encode a specific characteristic of an item presented in one position on the Visual Field and loosely connected when
they present the same characteristics but items presented in different
positions on the visual field. Moreover, the model incorporates a number of synaptic currents and processes that allowed us to successfully
simulate the Visual Search experiment [4, 5]. In research, cultural membership is usually investigated between collectivists (Asian cultures)
and individualists’ groups (Western Europeans cultures) [1, 2]. The
experiments that bsSoTS simulated so far are based on individualists’
groups [4, 5]. To simulate therefore the difference in behavior between
collectivists and individualists, we need to simulate the difference
observed in collectivists cultures. To do that we tested the coupling
between the neurons that encode a specific item presented in one
position on the Visual Field as a saliency parameter. The same parameter was used in preliminary modelling work in our lab [3].
The results showed that the saliency parameter successfully simulates
the behavioral results. Additionally, further behavioral work is proposed by investigating the relationship between the different saliency
levels and the observed effect.
References
1. Nisbet RE, Masuda T: Culture and point of view. Proceedings of the National
Academy of Sciences of the United States of America 2003, 100: 11163–11170.
2. Nisbet RE, Peng K, Choi I, Norenzayan A: Culture and systems of thought:
Holistic versus analytic cognition. Psychological Review 2001, 108: 291–310.
3. Mavritsaki E, Rentzelas P: Cross-cultural differences in visual attention: A
computational modelling study. BMC Neuroscience, 16: 204.
4. Mavritsaki E, Humphreys GW: Temporal binding and segmentation in
Visual Search: A computational neuroscience analysis. Journal of Cognitive
Neuroscience 2015, 28: 1553–1567
5. Mavritsaki E, Heinke D, Allen HA, Deco G, Humphreys GW: Bridging the
gap between physiology and behavior: Evidence from the sSoTS model of
human visual attention. Psychological Review 2011, 118: 3–41.
P216
NeuroNLP: a natural language portal for aggregated fruit fly brain
data
Nikul H. Ukani1, Adam Tomkins2, Chung-Heng Yeh1, Wesley Bruning3,
Allison L. Fenichel4, Yiyin Zhou1, Yu-Chi Huang5, Dorian Florescu2, Carlos
Luna Ortiz2, Paul Richmond6, Chung-Chuan Lo5, Daniel Coca2, Ann-Shyn
Chiang5, Aurel A. Lazar1
1
Department of Electrical Engineering, Columbia University, New York, NY
10027, USA; 2Department of Automatic Control & Systems Engineering,
BMC Neurosci 2017, 18(Suppl 1):60
The University of Sheffield, Sheffield, S1 3JD, UK; 3Department
of Computer Science, Columbia University, New York, NY 10027, USA;
4
Data Science Institute, Columbia University, New York, NY 10027, USA;
5
Brain Research Center, National Tsing Hua University, Hsinchu 30013,
Taiwan; 6Department of Computer Science, The University of Sheffield,
Sheffield, S1 4DP, UK
Correspondence: Aurel A. Lazar (aurel@ee.columbia.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P216
NeuroNLP, a key application on the Fruit Fly Brain Observatory [1] platform (FFBO, http://fruitflybrain.org), provides a modern web-based
portal for navigating fruit fly brain circuit data. Increases in the availability and scale of fly connectome data demand new, scalable and
accessible methods to facilitate investigation into the functions of
the complex circuits being uncovered. Combining data from multiple sources into a single database, with a common data model, NeuroNLP facilitates access to data from various sources simultaneously.
It is built on top of the NeuroArch database [2] which codifies fly connectome data from both the FlyCircuit database [3] and the Janelia
Fly Medulla data [4]. The former hosts meso-scale connectome data
on the whole-brain level and the latter contains detailed, micro-scale
synaptic information about the Medulla neuropil. NeuroNLP allows
users to probe biological circuits in the NeuroArch database with plain
English queries, such as “show glutamatergic local neurons in the left
antennal lobe” and “show neurons with dendrites in the left mushroom and axons in the fan-shaped body”, replacing the cumbersome
menus prevalent in today’s neurobiological databases. This enables
in-depth exploration and investigation of the structure of brain circuits, using intuitive natural language queries that are capable of
revealing latent structure and information. Equipped with powerful
3D visualization, NeuroNLP standardizes tools and methods for graphical rendering, representation, and manipulation of brain circuits, while
integrating with existing databases such as the FlyCircuit. It currently
supports queries to show, add, filter and remove neurons based on 1)
the parent neuropil, 2) neuron type (local or projection), 3) dendritic/
axonal arborization, 4) neurotransmitter and 5) related postsynaptic
or presynaptic neurons. The graphical user interface complements the
natural language queries with additional controls for exploring neural
circuits. Designed with an open-source, modular structure, it is highly
scalable and extensible to additional databases and languages. Accessible through a laptop or smartphone (Figure 1) at https://neuronlp.
fruitflybrain.org, NeuroNLP significantly increases the accessibility of
fruit fly brain data, streamlining the way we explore and interrogate
distal data sources to open new avenues of research, and enrich neuroscience education.
Figure 1. Smartphone screenshot of NeuroNLP showing 16 lobula
plate tangential cells. Each neuron can be cross-linked to the FlyCircuit Database (left panel)
Page 125 of 176
References
1. Ukani NH, Yeh C-H, Tomkins A, Zhou Y, Florescu D, Ortiz CL, Huang Y-C,
Wang C-T, Richmond P, Lo C-C et al., The Fruit Fly Brain Observatory: from
structure to function. Neurokernel Request for Comments, Neurokernel RFC
#7, 2016. DOI: 10.1101/092288.
2. Givon LE, Ukani NH, Lazar AA, NeuroArch: A Graph dB for Querying and
Executing Fruit Fly Brain Circuits, Neurokernel Request for Comments, Neurokernel RFC #4, 2015. DOI: 10.5281/zenodo.31947.
3. Chiang A-S, Lin C-Y, Chuang C-C, Chang H-M, Hsieh C-H, Yeh C-W, Shih
C-T, Wu J-J, Wang G-T, Chen Y-C et al., Three-dimensional reconstruction
of brain-wide wiring networks in Drosophila at single-cell resolution. Cell
2011, 21(1):1–11.
4. Takemura S, Xu CS, Lu, Z, Rivlin PK, Parag T, Olbris DJ, Plaza S, Zhao T,
Katz WT, Umayam L et al., Synaptic circuits and their variations within
different columns in the visual system of Drosophila. PNAS 2015,
112(44):13711–13716.
P217
Towards prediction of plasticity response to paired cTBS
from resting state network connectivity
Bahar Moezzi1, Brenton Hordacre1, Mitchell R. Goldsworthy1,2, Michael C.
Ridding1
1
Robinson Research Institute, School of Medicine, University of Adelaide,
Adelaide, Australia; 2Discipline of Psychiatry, School of Medicine,
University of Adelaide, Adelaide, Australia
Correspondence: Bahar Moezzi (bahar.moezzi@mymail.unisa.edu.au)
BMC Neuroscience 2017, 18 (Suppl 1):P217
Paired continuous theta burst stimulation (cTBS) is a non-invasive
brain stimulation technique that can induce neuroplastic change in
the primary motor cortex [1]. The response shows high intersubject
variability and having a marker that might predict response would
be useful in many situations. Our hypothesis is that a more strongly
connected cortical network is associated with a greater plasticity
response. To test this hypothesis, we quantify the correlation between
graph theoretical measures of EEG connectivity data and the plasticity response to paired cTBS. We use state of the art methodologies in
order to provide biological markers of response to paired cTBS to be
used in their prediction.
We tested eighteen healthy adults (8 male, 1left handed) with a mean
age of 24.2 (SD 6.0). Three minutes of continuous resting state EEG
with open eyes was acquired. Baseline MEPs (n = ?) were recorded
and then paired cTBS was applied to the left primary motor cortex,
followed by three blocks of 20 TMS pulses. Surface EMG was used to
record the motor evoked potential from the right first dorsal interosseous (FDI) muscle. We preprocessed EEG data and removed artefacts.
Graph theory provides a method to characterize the brain as a set of
nodes interconnected by a set of edges [2]. It is suggested that an
intracortical electrical source approach in graph theoretical analysis
of EEG data is superior to the analysis at the surface level. Debiased
weighted phase lag index is used as a measure of functional connectivity in the source space among the regions of interest. The connectivity matrix is thresholded and a graph is constructed. Several graph
theoretical measures including degree, density, distance, clustering
coefficient and characteristic path length are computed. Each participant’s plasticity response to paired cTBS is correlated with that participant’s graph theoretical measures (at each region of interest).
Preliminary analysis shows that the distance from the site of stimulation associates with the response to paired cTBS, while degree, density, clustering coefficient and characteristic path length do not. These
findings suggest that graph theoretical measures of network connectivity may have some utility in predicting the neuroplasticity response
to paired cTBS.
BMC Neurosci 2017, 18(Suppl 1):60
Page 126 of 176
References
1. Goldsworthy MR, Pitcher JB, Ridding MC: Neuroplastic modulation of inhibitory motor cortical networks by spaced theta burst stimulation protocols.
Brain stimul 2013, 6:340–345.
2. Bullmore ET, Sporns O: Complex brain networks: graph theoretical analysis
of structural and functional systems. Nature Rev Neurosci 2009, 10:186–98.
P218
Mathematical Analysis of Transient “domino effect” like Brain
Dynamics
Jennifer L. Creaser1, Congping Lin1, Peter Ashwin1, Jonathan T. Brown2,
Thomas Ridler2
1
Department of Mathematics, University of Exeter, Exeter, EX4 4QD, UK;
2
Institute of Biomedical and Clinical Sciences, University of Exeter Medical
School, Exeter, EX4 4PS, UK
Correspondence: Jennifer L. Creaser (j.creaser@exeter.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P218
There has been much research into complex neurological diseases
such as, for example, epilepsy and Alzheimer’s disease, however
much remains unknown. It has become clear that such diseases are
associated with abnormal brain network function including hyperexcitability. Brain network models used to study excitability, are often
characterized by different dynamic regimes, such as alternating rest
and excited states. The transient dynamics responsible for transitions
between dynamic states are often discounted or overlooked in favour
of the long term asymptotic behaviour. However, analysis of these
transitions is instrumental in understanding, for example, the onset
and evolution of epileptic seizures.
We consider a model of seizure initiation represented by a network of
diffusively coupled bi-stable neurones driven by noise. Nodes in the
network can switch between the quiescent attractor and active attractor due to noise fluctuations. We focus on the case of sequential escapes
of nodes and the associated escape times. Understanding the factors
controlling sequential transitions between stable/unstable attractors
is important as they have been implicated in a diverse range of brain
functions associated with neuronal timing, coding, integration as well
as coordination and coherence [1, 2]. Network properties such as the
coupling and excitability of nodes in such systems can promote (or suppress) escape of others on the network. We aim to quantify and characterise the escape times in terms of the coupling and excitability of
nodes.
We apply our theoretical framework to investigate escape times to the
propagation of epileptiform activity in parasagittal brain slices containing mouse medial entorhinal cortex (mEC). We observe sequential
recruitment of electrodes to the ictal-like state and can determine the
escape time, that is the equivalently average burst start time of each
electrode. The sequential recruitment of electrodes to the ictal-like
state could be seen as sequential escapes to an excited state in the
underlying functional brain networks. We explore differences in intrinsic (node) excitability across the mEC by incorporating an excitability
gradient into our prototypical bi-stable model. Figure 1 shows preliminary findings comparing the average burst start time observed in
experiments (grey) and computed with the bi-stable model (black). In
this presentation, I will address the question how a network’s structure
and its properties influence sequential recruitment/escape of nodes in
a network.
Figure 1. The average start time of ictal activity relative to ventralmost channel recorded from along the dorso-ventral axis of the
mEC in vitro using a 16-shank silicon probe array (grey) with the
average start time for each channel computed using 1000 simulations of a unidirectionally coupled 16 node bi-stable system with a
linear excitability gradient (black)
References
1. Rabinovich, MI, Pablo V: Robust transient dynamics and brain functions.
Front Comput Neurosci 2011, 5: 24–33.
2. Rabinovich, MI, Ramon H, Gilles L: Transient dynamics for neural processing.
Science 2008, 321(5885): 48–50.
P219
Synchronized neocortical dynamics during NREM sleep
Daniel Levenstein1,2, Brendon O. Watson2,3, György Buzsáki1,2, John
Rinzel1,4
1
Center for Neural Science, New York University, New York, NY, 10003,
USA; 2NYU Neuroscience Institute, New York University, New York, NY,
10016, USA; 3Dept. of Psychiatry, Weill Cornell Medical Center, New York,
NY, 10065, USA; 4Courant Institute for Mathematical Sciences, New York
University, New York, NY, 10012, USA
Correspondence: Daniel Levenstein (dl2820@nyu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P219
During periods of behavioral quiescence such as NREM sleep, quiet
wakefulness, and under anesthesia, neocortical populations can show
‘synchronized dynamics’ [1]: low-frequency alternations between lowrate spiking (UP states) and population-wide inactivity (DOWN states).
Previous work has indicated that these dynamics are mediated by
the interaction of recurrent excitation and neuronal adaptation [1–3].
Using a Wilson-Cowan model (Figure 1A), we show that synchronized
regimes are seen during low levels of drive to a recurrent adapting
neural population. Due to the possibility for both noise-induced and
adaptation-induced transitions, this type of oscillation can show a
BMC Neurosci 2017, 18(Suppl 1):60
range of spectral properties and UP/DOWN state dwell time statistics,
which fit into 4 broad classes of synchronized regimes (Figure 1B).
Using a nonparametric distribution-matching method, we find that
this idealized model is able to reproduce the dwell time statistics
of UP/DOWN states from multiple behavioral contexts in vivo.
During NREM sleep [4], DOWN states are coincident with large deflections in the LFP/EEG in a stereotyped pattern termed the ‘slow oscillation’. Unlike synchronized dynamics in other behavioral states (e.g.
[5]), we find that the NREM slow oscillation is best represented by an
‘ExcitableUP’ regime, in which noise or perturbation of a stable UP state
can induce brief DOWN states (Figure 1C). Our model reveals a mechanistic basis for multiple features of NREM sleep that are thought to
be related to mnemonic and homeostatic functions [6]: impulse-initiated slow waves and sequential activity at the DOWN->UP transition
accompanied by gamma-band activity.
Figure 1. Synchronized dynamics in an adapting Wilson-Cowan
model. A. Model schematic and equations. B. Synchronized
regimes available to the model. (Left) Phase plane. (Right) Simulated time courses and dwell time distributions. C. State diagram in
I-W reveals parameter domain for each synchronized regime. Color
indicates similarity to NREM sleep. Solid/dashed line: saddle-node/
Hopf bifurcations
References
1. Harris KD, Thiele A: Cortical state and attention. Nature Reviews Neuroscience
2011. 12(9):509–523.
2. Parga N, Abbott LF: Network model of spontaneous activity exhibiting
synchronous transitions between up and down States. Frontiers in Neuroscience 2007; 1(1):57–66.
3. Compte A, Sanchez-Vives MV, McCormick DA, Wang XJ: Cellular and
network mechanisms of slow oscillatory activity and wave propagations in
a cortical network model. J. Neurophys 2003; 89(5):2707–2725.
4. Watson BO, Levenstein D, Greene JP, Gelinas JN, Buzsáki G: Network
Homeostasis and State Dynamics of Neocortical Sleep. Neuron 2016;
90(4):839–852.
5. Mochol G, Hermoso-Mendizabal A, Sakata S, Harris KD, de la Rocha, J: Stochastic transitions into silence cause noise correlations in cortical circuits.
PNAS 2015; 112(11):3529–3534.
6. Levenstein D, Watson BO, Rinzel J, Buzsáki G. Sleep regulation of the distribution of cortical firing rates. Current Opinion in Neurobiology 2017. In press.
P220
Accumulation process and multi‑layer mechanisms of perceptual
alternation in auditory streaming
Rodica Curtu1, Anh Nguyen1, John Rinzel2
1
Department of Mathematics, The University of Iowa, Iowa City, IA 52242,
USA; 2Courant Institute of Mathematical Sciences, New York University,
New York, NY 10003, USA
Correspondence: Rodica Curtu (rodica-curtu@uiowa.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P220
Page 127 of 176
In daily life, the auditory system sorts the mixture of sounds from different sources into specific acoustic information by grouping acoustic
events over time and forming internal representations of sound streams.
A particular set of stimuli that have been used intensively to study that
phenomenon consists of sequences of alternating high (A) and low (B)
pure tones presented as repeated triplets, ABA_ABA_….Depending on
the frequency separation (df) between the two tones, subjects report
either of two percepts: “integration” (a single, coherent stream of high and
low tones, like a galloping rhythm) or “segregation” (two parallel distinct
streams). In our lab, the psychophysical experiment was conducted on
15 human subjects of normal hearing. They were prompted to listen to
repeating sequences of ABA_ triplets at df = 3, 5, 7 semitones difference,
with a total of 675 trials per df condition. Each sequence was comprised
of sixty 500 ms-long triplets, resulting in a 30 s-long presentation. Subjects
were asked to press and hold different buttons on a keypad when they
perceived integration and segregation, respectively. Data analysis revealed
time course and statistical distribution of perceptual switching. After the
stimulus onset, it takes several seconds for the trial-averaged probability of
stream segregation to build up, and the first percept is typically integration.
Also, subjects report spontaneous alternations between the two percepts,
and the percept durations are gamma-distributed. Furthermore, a previous study reveals that there are similarities between build-up functions
of stream segregation from psychophysical experiments (psychometric
functions) and those from multi-unit recordings from monkeys’ primary
auditory cortex (area A1) (neurometric functions) [1]. In this presentation,
we first demonstrate that a signal-detection model introduced in [1] to
compute neurometric functions, is not sufficient to produce realistic percept durations as reported experimentally. In particular, mean spike counts
extracted from cortical recordings [1] were used to generate neuronal
responses, which were used as inputs to a signal-detection model. We
showed that this model produces percept durations whose distribution is
exponential (not gamma) and whose means are significantly smaller than
those reported experimentally. We propose an extension to this model in
the form of a multi-stage feedforward auditory network with components:
i) area “A1” whose local outputs (mean spike counts) are subject to threshold-based binary classifiers (binary neurons); ii) An ensemble of binary neurons (BN) receiving local input from “A1”; and iii) Two competing units (“the
accumulators”) whose activities depend on accumulated evidence from
neuronal ensemble BN for each of the two percepts, integration and segregation. The suppressed neuronal unit accumulates evidence against the
current percept while the dominant unit gradually reduces its activity. Both
are drifting towards their given thresholds.
Conclusion: The proposed evidence accumulation model is able to reproduce qualitatively and quantitatively switching behavior between integration and segregation in auditory streaming. At each df the model produced
percept durations whose distribution is gamma-like and whose means are
comparable to those obtained in our psychophysical experiment.
Acknowledgements
This material is based upon work supported by the National Science
Foundation under Grant Number CRCNS 1515678
References
1. C. Micheyl, B. Tian, R. Carlyon, R. Rauschecker: Perceptual organization of
tone sequences in the auditory cortex of awake macaques. Neuron 2005,
48:139–148.
2. D. Barniv, I. Nelken: Auditory streaming as an online classification process
with evidence accumulation. PLOS ONE 2015.
3. R. Cao, A. Pastukhov, M. Mattia, J. Braun: Collective Activity of Many Bistable
Assemblies Reproduces Characteristic Dynamics of Multistable Perception.
J Neurosci 2016, 36(26):6957–6972.
P221
The Necessity of Sleep and Wake: Synaptic Homeostasis
via System‑Level Plasticity and the Ascending Arousal System
Sahand Assadzadeh1,2, Peter A. Robinson1,2
1
School of Physics, The University of Sydney, NSW 2006, Sydney, Australia;
2
Center for Integrative Brain Function, The University of Sydney, NSW
2006, Sydney, Australia
Correspondence: Sahand Assadzadeh (sahanda@physics.usyd.edu.au)
BMC Neuroscience 2017, 18 (Suppl 1):P221
BMC Neurosci 2017, 18(Suppl 1):60
One of the important functions of sleep is believed to be the regulation of synaptic weights in the brain. Mounting experimental evidence
has found that on average, synapses that are upscaled during wakefulness are downscaled during sleep, providing a possible mechanism through which synaptic stability is maintained in the brain. This
is often referred to as the synaptic homeostasis hypothesis (SHH) [1].
However, the questions of how and why sleep is necessary to fulfill
this function remain unanswered. Neural field theory (NFT) has shown
that synaptic plasticity dynamics depend strongly on network level
effects, such as the overall system frequency response, with especially
enhanced plasticity at resonances [2]. NFT is used to study the systemlevel effects of plasticity in the corticothalamic system, where arousal
states are represented parametrically by the connection strengths of
the system, among other physiologically based parameters (Fig. 1).
Here it is found that the plasticity dynamics have no fixed points or
closed cycles in the parameter space of the connection strengths; but
parameter subregions exist where flows have opposite signs. Remarkably, these subregions coincide with previously identified regions
corresponding to wake and slow-wave sleep, thus demonstrating the
role of state-dependent activity on the sign of synaptic modification.
We then show that a closed cycle in the parameter space is possible
by coupling the plasticity dynamics to that of the ascending arousal
system (AAS), which moves the brain back and forth between sleep
and wake, and thus between the opposite-flow subregions to form a
closed loop. In this picture, both wake and sleep are necessary to stabilize connection weights in the brain, because each modifies synaptic
strengths in an opposite direction relative to the other.
Figure 1. Evolution of connection strengths around a wakesleep cycle forming a closed loop in arousal state space. The blue
line represents plastic effects during wakefulness that result an
increase of the corticothalamic and corticocortical loop gains in
the corticothalamic system, with red lines corresponding to the
opposite effect observed during slow-wave sleep. Thin lines indicate the action of the AAS in switching between wake and sleep
states
Acknowledgements
This work was supported by the Australian Research Council under
Center of Excellence for Integrative Brain Function Grant CE140100007
and Laureate Fellowship Grant FL140100025.
Page 128 of 176
References
1. Tononi G, Cirelli C. Sleep and the Price of Plasticity: From Synaptic and Cellular Homeostasis to Memory Consolidation and Integration. Neuron. 2014;
81(1): 12–34.
2. Robinson PA. Neural field theory of synaptic plasticity. J Theor Biol. 2011;
285(1): 156–163.
P222
Low‑ and high‑mode waking states in the corticothalamic system
Paula Sanz-Leon1,2, Peter A. Robinson1,2
1
School of Physics, University of Sydney, Sydney, New South Wales,
Australia; 2Center for Integrative Brain Function, University of Sydney,
Sydney, New South Wales, Australia
Correspondence: Paula Sanz-Leon (paula.sanz-leon@sydney.edu.au)
BMC Neuroscience 2017, 18 (Suppl 1):P222
A neural field model of the corticothalamic system has multistable regions
of five steady-state solutions, up to three of which are linearly stable [1];
and, up to two of which lie within firing rate levels that are considered
moderate, yet normal, in adult human physiology [2]. This confirms the
existence of additional arousal states beyond the traditional steady states
which have been identified with either normal or seizure-like activity [2].
The signature of these additional states, which we call H-mode states, is
an overall increased level of activity up to 35 s−1 [blue dots in Figs 1(a) and
1(b)] with respect to the canonical waking states, or L-mode states (black
dots). More specifically, compared to the L-states (illustrated as black
dots), the H-states exhibit enhanced thalamic activity. In Fig. 1(c) mean firing rates are arranged in parallel coordinates where the coordinates correspond to cortical (φe), reticular (φr), and relay nuclei (φs) firing rates. This
type of plot allows for the identification of trends within a group, and for
the comparison with another group. Here, we observe that the qualitative behavior of the H-states (blue lines) is similar to the one of the L-states
(black lines): φe < φr and φs < φr. However, in the H-states, despite the large
dispersion of relay activity, cortical activity remains relatively constant. In
Fig. 1(d), we show the power spectra for both L- and H-states (illustrated in
black and blue lines, respectively). The H-states (i) have higher power density than the L-states over all the frequency range (0 < f < 45 Hz); and (ii)
have a 5-order of magnitude increase in the power in the high-beta and
gamma bands (20-35 Hz) with respect to the baseline spectra of waking
states. This last result is consistent with focused and hyperarousal states
found in the literature [3]. In hyperarousal increased thalamic activity is
linked to high levels of attention and gamma enhancements expected
due to increased activity in the relay nuclei of the thalamus.
Figure 1. Comparison of L-mode states and H-mode states from
multistable regions of the corticothalamic system. Black dots and
lines correspond to properties of L-states (fa < 20 s−1), while blue
dots and lines are those of the H-states (fa around 30 s−1). Panels A
and B are the steady states in φe-φr and φe-φs space, respectively.
Panel C shows a parallel coordinate plot of the corticothalamic firing
rates. Panel D shows the spectral signature the L-states and H-states
BMC Neurosci 2017, 18(Suppl 1):60
References
1. Sanz-Leon P and Robinson PA: Multistability in the corticothalamic system. J.
Theor. Biol. 2017 (under review)
2. Robinson PA, Rennie CJ, Wright JJ, Bahramali H, Gordon E, Rowe DL: Prediction of electroencephalographic spectra from neurophysiology. Phys. Rev. E
2001; 63:021903.
3. GrØnli J, Rempe MJ, Clegern WC, Schmidt M and Wisor JP. Beta EEG reflects
sensory processing in active wakefulness and homeostatic sleep in quiet
wakefulness. J. Sleep Res. 2001; 25:257–268.
P223
Closed‑loop temporally structured light stimulation in weakly
electric fish
Caroline G. Forlim1,2, Lírio O. B. de Almeida 3, Ángel Lareo4, Reynaldo D.
Pinto3, Pablo Varona4, Francisco B. Rodríguez4
1
Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical
Center Hamburg-Eppendorf, Hamburg, 20246, Germany; 2Departamento
de Física Geral, Universidade de Sao Paulo, Sao Paulo, 05508-090, Brazil;
3
Instituto de Física de Sao Carlos, Universidade de Sao Paulo, Sao Carlos,
13560-970, Brazil; 4Escuela Politécnica Superior, Universidad Autonoma
de Madrid, Madrid, 28049, Spain
Correspondence: Caroline G. Forlim (c.garcia-forlim@uke.de), Francisco
B. Rodríguez (f.rodriguez@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P223
Closed-loop stimulation is a promising technique for neuroscience
studies, especially in behavioral experiments [1, 2]. Weakly electric fish
discharge short electric pulses or waves through an electric organ and
detect small changes in the electric field using electroreceptors [1, 3].
These fish live in turbid waters and use electrical sensing as an additional sense to increase visual details. In addition, their electric pulses
are also used to communicate by changing their inter pulse intervals
depending on the behavioral context [3]. Recently, attention has been
paid to the visual system [4]. However, most experiments assessing
vision were conducted with periodic flashlights lasting just a few seconds and moreover, in restrained animals.
We developed the first closed-loop setup that uses temporally structured light as a stimulus for long periods in freely swimming fish. In
these closed-loop protocols, the light pulses are triggered based on
the real time monitored electrical activity, resulting in stimulus with
similar complex temporal structure as the electrical signaling of the
fish. The setup can be easily adapted to different stimulus modalities
such as mechanical, acoustic and electrical stimulation allowing studies of multisensory integration.
Our validation protocol consisted of 15 min control session followed
by 15 min light pulse stimulation in Gnathonemus petersii. The light
stimuli were either triggered by the fish’s own electrical activity and
therefore with complex temporal structure or periodic. It is important
to emphasize that the main differences between these two stimuli is
the temporal structure, the closed-loop share similar complex temporal structure as the electrical signaling and the periodic does not, no
temporal structure is encoded in the light stimulus. We show that, for
long light stimulation periods, fish decreased the discharge rate. The
decrease in discharge was more accentuated when light stimuli were
triggered by the fish’s electrical activity as opposed to periodic stimuli, meaning that probably the information encoded in the temporal
structure was somehow meaningful for the fish and that the brain processed it distinctly from a simple periodic structure.
To the best of our knowledge, this is the first study on how light can
influence the fish electrical system for long periods of time. The results
give rise to important questions on the influence of light in electrocommunication and the processing of multisensory information,
which can be addressed using the proposed methodology.
Acknowledgements
This work was funded by Spanish projects of Ministerio de Economía
y Competitividad/FEDER TIN2014-54580-R, DPI2015-65833-P, ONRG
grant N62909-14-1-N279, Spanish-Brazilian Cooperation PHB20070008 and 7ª Convocatoria De PROYECTOS de COOPERACION
INTERUNIVERSITARIAUAM-SANTANDER con America Latina and Brazilian Agency of Conselho Nacional de Desenvolvimento Científico e
Page 129 of 176
Tecnológico and Fundação de Amparo à Pesquisa do Estado de São
Paulo.
References
1. Forlim CG, Pinto RD, Varona P, Rodríguez FB. Delay-Dependent Response
in Weakly Electric Fish under Closed-Loop Pulse Stimulation. PLoS ONE
2015;10:e0141007. doi:10.1371/journal.pone.0141007.
2. Lareo A, Forlim CG, Pinto RD, Varona P, Rodriguez F. de B. Temporal CodeDriven Stimulation: Definition and Application to Electric Fish Signaling.
Front Neuroinform 2016;10:41. doi:10.3389/fninf.2016.00041.
3. Bullock TH, Hopkins CD, Popper AN, Fay RR, editors. Electroreception. vol.
21. Springer New York; 2005.
4. Pusch R, Kassing V, Riemer U, Wagner HJ, von der Emde G, Engelmann J. A
grouped retina provides high temporal resolution in the weakly electric
fish Gnathonemus petersii. J Physiol Paris 2013;107:84–94.
P224
Information‑theoretic analysis of temporal code‑driven stimulation
applied to electroreception
Ángel Lareo1, Caroline Garcia Forlim2, Reynaldo D. Pinto3, Pablo Varona1,
Francisco B. Rodríguez1
1
Grupo de Neurocomputación Biológica, Departamento de Ingeniería
Informática, Escuela Politécnica Superior, Universidad Autónoma
de Madrid, Madrid, Spain; 2Clinic and Policlinic for Psychiatry
and Psychotherapy, University Medical Center, Hamburg-Eppendorf,
Hamburg, Germany; 3Lab. Neurodynamics/Neurobiophysics - Dept.
Physics and Interdisciplinary Sciences - Institute of Physics of São Carlos,
Universidade de São Paulo, São Paulo, Brazil
Correspondence: Ángel Lareo (angel.lareo@uam.es), Francisco B. Rodríguez (f.rodriguez@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P224
Biological systems can encode information in a sequential manner, and
temporal encoding gives rise to complex temporal patterns of activity. Thus, information processing in those systems can be analyzed
studying the temporal structure of event trains. This is the approach
followed by a recently defined real time stimulation methodology, temporal code-driven stimulation (TCDS) [1]. TCDS is a closed-loop stimulation protocol that first digitizes and binarizes a biological signal and
then delivers the stimulus when a predefined code is detected. This
code represents the sequential activity in the signal whose meaning
is the goal of the system study. The methodology can use the study
of changes in the information processing of a given biological system
among different sessions: code-driven stimulation sessions, control sessions without stimulation and open-loop stimulation sessions.
In order to test this methodology, an implementation of TCDS using
hard real time has been applied to electroreception using the weakly
electric fish Gnathonemus Petersii. The electromotor neurons of this
animal generate electrical signal pulses which can be measured in a
water tank using appropriate hardware [2, 3]. These signals follow
a temporal coding scheme [4] where information is encoded in the
inter-pulse interval (IPI) [5]. Thus, it constitutes a convenient animal
model to test closed-loop stimulation methods in an alive and freelybehaving biological system. The TCDS protocol binary digitizes the
signal of the fish detecting the presence or absence of a pulse event
during the binarization period and uses this codification to stimulate
after detecting a preselected code from the fish’ activity. Analysis of
information processing in weakly electric fish is done in previous studies in terms of IPIs distribution [1].
We complement the analysis of the TCDS protocol with a measure
based on information theory: Transitions between codes. As a proof
of concept, we used 4-bit codes and selected as the trigger a code
with mean probability of occurrence during control sessions. Codes
were grouped by the number of pulses in them, defining three sets:
low, medium and high number of pulses. Preliminary results applying
TCDS to electroreception in weakly electric fish indicates that it distinctly conditions the response of the system when stimulating after
a predetermined code. This conclusion is also drawn by analyzing the
probability of transitions between codes, as an increase in low-low
transition probability is detected when the system is stimulated with
the code 0101.
BMC Neurosci 2017, 18(Suppl 1):60
Page 130 of 176
Acknowledgements
We acknowledge support from MINECO/FEDER TIN2014-54580-R,
DPI2015-65833-P (http://www.mineco.gob.es/) and ONRG grant
N62909-14-1-N279.
Acknowledgements
This research was supported by TIN2014-54580-R, BES-2011-049274,
NIH grant R01GM113967 and CNPq grant 234817/2014-3.
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1. Lareo A, Forlim CG, Pinto RD, Varona P, Rodriguez F: Temporal Code-Driven
Stimulation: Definition and Application to Electric Fish Signaling. Frontiers
in Neuroinformatics 2016, 10:41.
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3. Forlim CG, Pinto RD, Varona P, Rodriguez FB: Delay-dependent response in
weakly electric fish under closed-loop pulse stimulation. 2015, 10.
4. Baker CA, Kohashi T, Lyons-Warren AM, Ma X, Carlson BA: Multiplexed
temporal coding of electric communication signals in mormyrid fishes. The
Journal of experimental biology 2013, 216:2365–2379.
5. Carlson BA: Electric signaling behavior and the mechanisms of electric
organ discharge production in mormyrid fish. Journal of Physiology-Paris
2002, 96:405–419.
P225
Gain control mechanism based on lateral inhibition of antennal
lobe improves pattern recognition performance under wide
concentration variability
Aaron Montero1, Thiago Mosqueiro2, Ramon Huerta1,2, Francisco B.
Rodriguez1
1
Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática,
Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid,
28049, Spain; 2BioCircuits Institute, University of California, San Diego, La
Jolla, CA 92093-0402, USA
Correspondence: Aaron Montero (aaron.montero.m@gmail.com),
Francisco B. Rodriguez (f.rodriguez@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P225
Many animals depend on odor information for living. Although different levels of concentration produce variation in the activation patterns
observed in olfactory receptor neurons, most animals can correctly
recognize the identity of odors regardless of their concentration. It
is not clear yet what mechanisms olfactory systems employ to recognize the same stimulus regardless of their concentrations. Experiments suggest that in insects this concentration invariance appears in
the Antennal Lobe, where the activity of Projection Neurons remains
nearly constant, even though the concentration changes [1]. One
hypothesis is that the Local Neurons are responsible to down regulate
the levels of activity (also known as gain control) by laterally inhibiting
the Projection Neurons [2]. We examine the impact of this gain control
mechanism on pattern recognition by designing a biologically plausible model based on the interactions between Local and Projection
Neurons. For this purpose, we used a computational model that represents the olfactory system of insects by a single hidden layer network
[3, 4, 5]. We consider three layers: Antennal Lobe, Kenyon cells and
Mushroom Body Output Neurons. In order to simulate the activation
patterns of Antennal Lobe for different concentration levels, we used
Gaussian functions with a variable height and width, where their centers encode the identity of the odor. We used datasets of 3000 patterns
divided into 10 pattern classes and 3 concentration levels. To model
the intrinsic variations observed in real olfactory systems, we added
a multiplicative white noise to these Gaussians with 3 different levels
(small, medium, large). The performance of a network with this gain
control mechanism presented significantly lower classification error
rate than a network without gain control, with an improvement of
~45%. A network with this gain control achieved a classification error
of ~0% for sets of patterns with small and medium noise and <5% for
large noise. These results suggest that gain control mechanism does
not only suppress outbursts of activity from input layers but also
greatly improves learning in Mushroom Bodies. Finally, because this
mechanism does not depend on any synaptic plasticity, in agreement
with the biological literature, it can also be applied to chemical sensors
in electronic devices for controlling changes in environmental conditions [6, 7].
References
P226
Maximum Relative Area as a Feature for Adaptability in ERP‑based
BCI Systems
Vinicio Changoluisa1,2, Pablo Varona1, Francisco B. Rodriguez1
1
Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática.
Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid,
Spain; 2Universidad Politécnica Salesiana, Quito, Ecuador
Correspondence: Vinicio Changoluisa (fchangoluisa@ups.edu.ec), Francisco B. Rodriguez (f.rodriguez@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P226
Adaptive Brain Computer Interfaces (BCI) are an important research
topic in the last years. However, a critical and pending problem is their
variable performance even within subjects. In event-related potentials
(ERP)-based BCIs the variability of amplitude and latency impair the
detection of the ERP components. In order to overcome those problems, target and non-target stimuli are repeated several times (trials).
Repetitions can cause fatigue and a decrease in task performance.
Therefore, achieving high accuracy with a few stimuli is a challenge.
We propose a methodology that contributes to the management of
variability in ERP-based BCIs through the characterization of the maximum relative voltage area (maxRAUC) in the region of the EEG signal
where a ERP component can be located. We call maxRAUC relative
since it is a maximum value within each trial, not the maximum value
of all trials. This method calculates maxRAUC incrementally in time for
each stimulus. The one with the highest value is considered a target
stimulus. In this way, the differences between a target and a non-target stimulus are maximized. Electrodes having the highest maxRAUC
in the ERP region of the signal are potentially likely to have better characteristics for detecting ERP effectively. Our method was tested with
a linear classifier (LDA) based on the Krusienski method (KM) [1] and
the dataset_IIb of the BCI competition (http://www.bbci.de/competition/ii/). This dataset contains the data of one user, divided into three
sessions: two training sessions (called 10 and 11) and one session to
test the classifier. Users were stimulated through P300 Speller Paradigm described in the competition. The electrodes with the largest
maxRAUC were found in the central and frontal lobes. We checked the
influence of these electrodes on the system’s adaptability and evaluated the classifier with two configurations: the first, with 8 electrodes
used in KM; and the second, by replacing Fz and Cz by the electrodes
among those with the higher maxRAUC of each session. With this electrode selection, the accuracy of the classifier improved and reached
100% success with a low number of trials, see Table 1. We also validated the robustness of our method by combining data from training
sessions 10 and 11.
BMC Neurosci 2017, 18(Suppl 1):60
Page 131 of 176
Table 1. Trials needed to achieve 100% success in each session.
Common Electrodes (CE): Pz, P3, P4, PO7, PO8, Oz. We emphasize
the best results with italic font
Session 10
Electrode
configura‑
tion
Elect.
KM
Cz + Fz +
electrodes CE
Session 11
Session 10 + 11
Trials
Elect.
Trials
Elect.
Trials
4
Cz + Fz + CE
12
Cz + Fz + CE
9
CE + 2
C1 + FPz + 3
maxRAUC CE
electrode
C1 + FC1 + CE 6
C3 + F1 + CE
4
CE + 1
C3 + CE
maxRAUC
electrode
F1 + CE
F1 + CE
5
3
9
In summary, here we propose a new methodology to extract additional information from EEG electrodes that contributes to manage
the adaptability of ERP-based BCIs. This method adapts to the variability of each session and helps to decrease the number of electrodes
and trials necessary to achieve a 100% success. The maxRAUC contributes to early detection of ERP and further adaptation. This method can
also be applied to other ERP components (N200, N100, etc.) which are
considered for future work.
Acknowledgements
This work was funded by Spanish projects of Ministerio de Economía
y Competitividad/FEDER TIN2014-54580-R, DPI2015-65833-P and Predoctoral Research Grants 2015-AR2Q9086 of the Government of Ecuador (SENESCYT).
References
1. Dean J Krusienski, Eric W Sellers, François Cabestaing, Sabri Bayoudh, Dennis
J McFarland, Theresa M Vaughan, and Jonathan R Wolpaw: A comparison of
classification techniques for the P300 Speller. Journal of neural engineering
2006, 3(4):299–305.
P227
Intrinsically stochastic neuron models for use in network
simulations
Vinícius L. Cordeiro, César C. Ceballos, Nilton L. Kamiji, Antonio C. Roque
Departamento de Física-FFCLRP, Universidade de São Paulo, Ribeirão
Preto, SP 14040-901, São Paulo, Brazil
Correspondence: Vinícius L. Cordeiro (vinicius.lima.cordeiro@usp.br)
BMC Neuroscience 2017, 18 (Suppl 1):P227
Experimental evidence suggest that neurons are inherently stochastic
systems displaying trial-to-trial response variability [1]. This stochasticity may have functional consequences on network behavior, so it
is important to construct stochastic single-neuron models to be used
in network simulations. There are basically two ways of constructing
a stochastic neuron model [2, 3]. One is to consider a deterministic
model, e.g. the leaky integrate-and-fire (LIF), Izhikevich or AdEx model
[2], and add stochastic terms to the inputs received by the neuron.
The other is to model a spike as an intrinsically stochastic event. The
second way can be implemented in two different but equivalent manners: by a randomly varying spike threshold as in the escape noise
model [4], or by a spike probability function Φ(V), which depends on
the membrane potential V as in the simplified version of the GalvesLöcherbach (GL) discrete-time model [5] recently proposed by Brochini et al. [3].
Here we have considered the Brochini et al. [3] version of the GL model
(from here onwards simply called GL model) and empirically determined the probability function Φ(V) so that the model can describe
stochastic firing behaviors of the two most import cortical cell types,
namely regular (RS) and fast (FS) spiking neurons [6]. To determine
Φ(V) for these two cell types, biophysically detailed models of RS and
FS neurons were chosen from the neuron database ModelDB (http://
senselab.med.yale.edu/modeldb/) and submitted to realistic patterns
of synaptic input. The detailed neuron model simulations were done
in NEURON [7]. These simulations generated time series of membrane
potential values Vt for the detailed RS and FS neuron models. From
these time series, we determined action potential onset values Vth
from the dV/dt versus V phase space using so-called Method II of [8].
For each action potential, the voltage values above threshold were discarded and with the remaining ones we constructed two distribution
histograms, one for all voltage values (including Vth) and the other for
threshold values only. The histograms were superposed as in Figure 12
of [9] to allow an estimate of the probability of firing for each discretization bin.
The resulting probability functions display nonlinear exponential
behavior. Based on them we constructed stochastic GL models for RS
and FS neurons and submitted them to simulated input currents to
obtain frequency-current (FI) curves. These stochastic neuron models
can be used in large-scale simulations of cortical network models.
Acknowledgements
This work was produced as part of the activities of FAPESP Research,
Disseminations and Innovation Center for Neuromathematics (grant
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially
supported by a CNPq fellowship (grant 306251/2014-0).
References
1. Longtin A: Neuronal noise. Scholarpedia 2013, 8(9):1618.
2. Gerstner W, Kistler WM, Naud R, Paninski L: Neural Dynamics: From Single
Neurons to Networks and Models of Cognition. Cambridge University Press
2014.
3. Brochini L, Costa AA, Abadi M, Roque AC, Stolfi J, Kinouchi O: Phase
transitions and self-organized criticality in networks of stochastic spiking
neurons. Sci Rep 2016, 6:35831.
4. Gerstner W, van Hemmen L: Associative memory in a network of ‘spiking’
neurons. Network 1992, 3:139–164.
5. Galves A, Löcherbach E: Infinite systems of interacting chains with memory
of variable length: a stochastic model for biological neural nets. J Stat Phys
2013, 151:896–921.
6. McCormick DA, Connors BW, Lighthall JW, Prince DA: Comparative
electrophysiology of pyramidal and sparsely spiny stellate neurons of the
neocortex. J Neurophysiol 1985, 54:782–806.
7. Carnevale NT, Hines ML: The NEURON Book. Cambridge University Press;
2006.
8. Sekerli M, Del Negro CA, Lee RH, Butera RJ: Estimating action potential
thresholds from neuronal time-series: new metrics and evaluation of methodologies. IEEE Trans Biomed Eng 2004, 51:1665–1672.
9. Azouz R, Gray CM: Cellular mechanisms contributing to response variability
of cortical neurons in vivo. J Neurosci 1999, 19:2209–2223.
P228
Modeling action potential and network effects after site‑directed
RNA editing of sodium channels
William W. Lytton1,2, Andrew Knox3, Joshua J. C. Rosenthal4
1
Depts. of Physiology & Pharmacology and Neurology, SUNY Downstate,
Brooklyn, NY 11203 USA; 2Dept. of Neurology, Kings County Hospital,
Brooklyn, NY 11203 USA; 3Dept. of Neurology, University of Wisconsin,
Madison, WI 53705 USA; 4Dept. of Neurobiology, Marine Biological
Laboratory, Woods Hole, MA 02543 USA
Correspondence: William W. Lytton (bill.lytton@downstate.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P228
New techniques now make it possible to modify messenger RNA
and thereby modify specific proteins in vivo. Experimentally, we have
edited RNA using adenosine deamination to modify the mammalian
fast sodium (Naf ) channel (NaV1.4) by converting a key lysine residue
to arginine in the selectivity region that is part of the aspartate-glutamate-lysine-alanine motif (DEKA to DERA). This change allows the
channel to be permeable to both Na and K, effectively changing the
reversal potential associated with this conductance to a value intermediate between the Nernst potentials of those two ions. The degree
of alteration in the Naf channel can be manipulated, producing a
mixed population of native and mutated channels. We modeled the
effects of this manipulation on the classical Hodgkin-Huxley model
BMC Neurosci 2017, 18(Suppl 1):60
Page 132 of 176
of action potential propagation in the squid axon, as well as in other
axonal models closer to mammalian morphology and temperature. As
expected, action potential amplitude was reduced at higher percentages of the modified Naf channel, reaching a point where an action
potential could no longer be maintained at the maximal conductance
provided. Action potential conduction velocity was fast (approximately 10 mm/ms) when using a high-impedance axon termination,
and showed little fall off with increased percent of modified channel.
Conduction velocity was much slower (approximately 2 mm/ms) when
using a low impedance termination, and showed a 20% falloff with
increase in percent of the modified channel. These results were seen
both at squid axon temperature and Ra (6.3o C and 34.5 O-cm) and
at mammalian values (37o C and 250 O-cm). Action potentials were
formed at lower sodium channel density and conducted at greater
velocity at the low temperature, where the more prolonged activation
due to the slower kinetics provided increased effect at neighboring
locations.
RNA editing is being used experimentally to erase the mutations that
introduce the premature termination codons that lead to cystic fibrosis. This manipulation has potential for clinical use in patients with this
deadly genetic disease. Similarly, clinical manipulation of the RNA for
the sodium channel has potential for use in intractable epilepsies such
as Lennox-Gastaux syndrome, where neither surgical nor pharmacological intervention is generally effective.
inter-regional phase-locking analysis by calculating the phase-locking
values (PLVs) from the EEG records of the two data sets mentioned
above. PLV measures the extent of instantaneous synchronization
between two distinct brain regions.
Our analysis revealed significant PLV in both age groups in the δ-θ
frequencies around movement onset. Invariant sub-networks were
established by strong PLV between brain areas involved in the motor
act, which were different in older and younger subjects. More intraand inter-hemispheric PLVs occurred in older than in younger subjects. Furthermore, data suggest that older subjects compensate for
the diminished connectivity observed between contralateral M1 and
SMA, and ipsilateral PM and SMA during movement preparation and
execution by establishing additional intra- and inter- hemispheric
connections.
Based on the above findings on local and inter-regional phase locking,
we built a mathematical model consisting of phase oscillators representing two main regions of the motor network, i.e. SMA and M1. This
simple model is capable of reproducing the effects of increased PLI
and, independently of this, the effect of increased PLV between both
regions. After extending the network model to all core motor regions
and fitting the model parameters to the experimental data it will serve
as a tool to make predictions on disturbed networks dynamics, e.g.
decoupling of nodes.
Acknowledgments
The authors would like to acknowledge NIH support from EB02290301
(WL), EB017695 (WL), MH086638 (WL), NS087726 (JR).
1. Popovych S, Rosjat N, Tóth TI, Wang BA, Liu L, Abdollahi RO, Viswanathan S,
Grefkes C, Fink GR, Daun S: Movement-related phase locking in the deltatheta frequency band. NeuroImage 2016, 139: 439–449.
2. Liu L, Rosjat N, Popovych S, Yeldesbay A, Wang BA, Tóth TI, Grefkes C, Fink
GR, Daun S: Movement related intra-regional phase locking in the deltatheta frequency band in young and elderly subjects. Program No. 624.08.
2016. Neuroscience Meeting Planner. San Diego, CA: Society for Neuroscience, 2016. Online.
P229
Movement‑related delta‑theta synchronization in young
and elderly healthy subjects
Silvia Daun1,2, Svitlana Popovych1,2, Liqing Liu1,2, Bin A. Wang1, Tibor I.
Tóth2, Christian Grefkes1,3, Gereon R. Fink1,3, Nils Rosjat1,2
1
Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3),
Research Center Juelich, Juelich, 52428 Germany; 2Heisenberg Research
Group of Computational Neuroscience - Modeling Neural Network
Function, Department of Animal Physiology, Institute of Zoology,
University of Cologne, Cologne, 50674, Germany; 3Department
of Neurology, University Hospital Cologne, Cologne, 50937, Germany
Correspondence: Silvia Daun (silvia.daun@uni-koeln.de)
BMC Neuroscience 2017, 18 (Suppl 1):P229
The wealth of data showing that human motor performance is
affected by normal ageing is contrasted by the dearth of data on ageing effects on the neural processes underlying action. For example, it
remains to be elucidated how the different phases of an action (i.e.,
preparation, initiation and execution) are expressed in neural oscillations and how these are affected by normal ageing. The interest in
ageing-related changes of motor performance and the neural basis
thereof are governed by the quest for more detailed insights into the
possible reorganization of the key phases of an action. For this reason,
it is apt and timely to study ageing-dependent effects on the neural
organization of motor performance in more detail. The crucial point of
such investigations is the study of synchronization, a key mechanism
underlying the coordination of distinct neural populations in shaping
complex motor tasks.
In an earlier EEG-study [1] on young adults, we found that when generating unilateral index-finger movements, local oscillations in the
δ-θ frequency band over the centroparietal, central and frontocentral
regions (corresponding to the primary motor area (M1), the supplementary motor area (SMA) and the pre-motor area (PM), respectively)
exhibited robust phase locking both prior to and during the movement. The local oscillations were most pronounced in the hemisphere
contralateral to the moving hand in both externally and internally triggered actions. A subsequent study [2] using an identical experimental
paradigm with a population of older adults found that the local phase
locking in the δ-θ frequency band was also present during the motor
acts of the older participants.
To investigate the neural processes underlying ageing-related
dependence of the motor performance in more detail, we employed
References
P230
ePyNN: a low cost embedded system for simulating Spiking Neural
Networks
Abraham Perez-Trujillo1, Andres Espinal2, Marco A. Sotelo-Figueroa2, Ivan
Cruz-Aceves3, Horacio Rostro-Gonzalez1
1
Department of Electronics, University of Guanajuato, 36885 Salamanca,
Guanajuato, Mexico; 2Department of Organizational Studies, University
of Guanajuato, 3625 Guanajuato, Mexico; 3CONACYT, Mathematics
Research Center (CIMAT), 36000 Guanajuato, Mexico
E-mail: Horacio Rostro-Gonzalez (hrostrog@ugto.mx)
BMC Neuroscience 2017, 18 (Suppl 1):P230
In this work, we present a low cost embedded system to simulate
Spiking Neural Networks through PyNN [1]. PyNN is a Python library
widely used in the neuroscience community to simulate at software
and hardware level several existent simulators (NEURON, NEST, PCSIM
and BRIAN) by acting as an interface to unify the different instructions and neuron model definitions. At hardware level, serves as a
high-level interface to directly map spiking neuron models on the
SpiNNaker neuromorphic system [2]. Albeit, SpiNNaker and other
systems such as TrueNorth have demonstrated tremendous capabilities to process information such as the brain does, these systems are
still unreachable for the large community who wants to implement
or validate simplest models on a hardware platform. In this regard,
we developed ePyNN, which is the PyNN simulator embedded on a
Raspberry Pi 3 board, which has a 1.2 GHz 64-bit quad-core ARMv8
CPU. Here, we have been able to implement a neural network with
the ≪ if_curr_exp ≫ model, which is a leaky integrate-and-fire model
with fixed threshold and exponentially-decaying post-synaptic conductance to generate real time locomotion patterns expressed as
spike trains for a hexapod robot [3, 4]. Specifically, we designed a network of 12 neurons, where each of them controls one of the degrees
of freedom (servomotors) of the robot with a specific topology, which
was offline performed by an evolutionary approach. Finally, the
ePyNN has been successfully validated on a real hexapod robot (Figure 1C) for three different locomotion gaits (walk, jog and run) running in real time (Figure 1 A, B).
BMC Neurosci 2017, 18(Suppl 1):60
Page 133 of 176
coherence intervals is evaluated with surrogate analysis, using “natural” surrogates (the hand acceleration recorded from other subjects),
as well as artificially constructed surrogates that have randomized
Fourier phases but match the power spectrum and value distribution
of the recorded time series [6]. We analyze separately the bilateral coupling of tremor amplitude, and evaluate its contribution to the bilateral coherence of tremor as assessed by spectral/wavelet coherence.
Figure 1 A. Biological Patterns B. Generated patterns C.
Robot + ePyNN platform
Acknowledgements
This research has been supported by the CONACYT project “Aplicación de la Neurociencia Computacional en el Desarrollo de Sistemas
Robóticos Biológicamente Inspirados” (No 269798).
References
1. Davison AP, Bruderle D, Eppler J, Kremkow J, Muller E, Pecevski D, Perrinet L,
Yger P: PyNN: A Common Interface for Neuronal Network Simulators. Front
Neuroinform 2008, 2:11.
2. Furber SB, Galluppi F, Temple S, Plana LA: The SpiNNaker Project. Proceedings of the IEEE 2014, 102(5):652–665.
3. Rostro-Gonzalez H, Cerna-Garcia PA, Trejo-Caballero G, Garcia-Capulin CH,
Ibarra-Manzano MA, Avina-Cervantes JG, Torres-Huitzil C: A CPG system
based on spiking neurons for hexapod robot locomotion. Neurocomputing
2015, 170:47–54.
4. Espinal A, Rostro-Gonzalez H, Carpio M, Guerra-Hernandez EI, Ornelas-Rodriguez M, Sotelo-Figueroa M: Design of Spiking Central Pattern Generators
for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution. Frontiers in Neurorobotics 2016, 10:6.
P231
Temporal structure of bilateral coherence in essential
and physiological hand tremor
Martin Zapotocky1,2, Soma Chakraborty1,2, Martina Hoskovcová2, Jana
Kopecká2, Olga Ulmanová2, Evžen Růžička2
1
Institute of Physiology, Czech Academy of Sciences, Prague, 14220,
Czech Republic; 2Department of Neurology, First Faculty of Medicine,
Charles University in Prague, 120 00, Czech Republic
Correspondence: Martin Zapotocky (zapotocky@biomed.cas.cz)
BMC Neuroscience 2017, 18 (Suppl 1):P231
Pathological hand tremor is associated with a number of neurological
diseases and may significantly impede motor functions in the patient.
The most common pathological type is essential tremor (ET), found in
4.6% of the population aged over 65 years [1]. The neurophysiological basis of ET is still under debate, and recent literature suggests that
patients with the ET diagnosis may in fact fall into several categories
with distinct disease origins [2]. Detailed quantitative analysis of the
features of the tremor may help in further classification and in clarifying the underlying neurophysiological mechanisms.
Depending on the underlying mechanism, the tremors in the left hand
and right hand may be coupled or independent. In the previous literature on tremors, this bilateral coupling was assessed using stationary
spectral coherence analysis, both on the level of hand kinematics and
of muscle activity. Highly prevalent bilateral coherence was found for
orthostatic [3] and psychogenic [4] tremors, while for other tremor
types including ET, such coupling was only rarely reported. In our
recent study [5], we used nonstationary, wavelet-based coherence
analysis of kinematic recordings to show that the oscillations of the
two hands are intermittently coupled in ET. We found that intervals of
strong bilateral coherence, lasting for up to a dozen seconds, alternate
with time intervals of insignificant coherence. We also observed intermittent bilateral coherence for physiological tremor (a normal hand
oscillation of low amplitude) recorded in healthy subjects.
Here we further extend the analysis of Ref. [5], based on the same
dataset of accelerometric recordings obtained from 34 ET patients
and 42 healthy subjects. We analyze the distribution of durations of
the bilaterally coherent time intervals extracted from wavelet analysis, and examine its dependence on the tremor type (physiological vs.
essential) and on the hand position. The statistical significance of the
Acknowledgements
Supported by Czech Science Foundation (P304/12/G069), Charles
University in Prague (Progres Q27, SVV NeST III), and Czech Health
Research Council (AZV 16-28119A).
References
1. Louis ED, Ferreira JJ: How common is the most common adult movement
disorder? Update on the worldwide prevalence of essential tremor. Mov
Disord 2010, 25(5):534–41.
2. Louis ED: Essential tremors: a family of neurodegenerative disorders? Arch
Neurol 2009, 66(10):1202–1208.
3. Lauk M, Köster B, Timmer J, Guschlbauer B, Deuschl G, Lücking CH. Side-toside correlation of muscle activity in physiological and pathological human
tremors. Clin Neurophysiol 1999, 110:1774–1783.
4. Raethjen J, Kopper F, Govindan RB, Volkmann J, Deuschl G: Two different pathogenetic mechanisms in psychogenic tremor. Neurology 2004,
63:812–815.
5. Chakraborty S, Kopecká J, Šprdlík O, Hoskovcová M, Ulmanová O, Růžička
E, Zapotocky M: Intermittent bilateral coherence in physiological and
essential hand tremor. Clin Neurophysiol 2017, 128(4):622–634.
6. Schreiber T, Schmitz A: Improved surrogate data for nonlinearity tests. Phys
Rev Lett 1996 77(4):635–638.
P232
Detecting joint pausiness in parallel spike trains
Matthias Gärtner1, Sevil Duvarci2, Jochen Roeper2, Gaby Schneider1
1
Institute of Mathematics, Goethe-University, Frankfurt, Germany;
2
Neuroscience Center, Institute of Neurophysiology, Goethe-University,
Frankfurt, Germany
Correspondence: Matthias Gärtner (gaertner@math.uni-frankfurt.de)
BMC Neuroscience 2017, 18 (Suppl 1):P232
Transient periods with reduced neuronal discharge - called ‘pauses’
- have recently gained increasing attention. In dopamine neurons,
pauses are considered important teaching signals, encoding negative
reward prediction errors. Particularly simultaneous pauses are likely to
have increased impact on information processing. Available methods
for detecting joint pausing analyze temporal overlap of pauses across
spike trains. Such techniques are threshold dependent and can fail to
identify joint pauses that are easily detectable by eye, particularly in
spike trains with different firing rates.
We introduce a new statistic called ‘pausiness’ that measures the degree of
synchronous pausing in spike train pairs and avoids threshold-dependent
identification of specific pauses. A new graphic termed the ‘cross-pauseogram’ compares the joint pausiness of two spike trains with its time shifted
analogue, such that a (pausiness) peak indicates joint pausing. When
assessing significance of pausiness peaks, we use a stochastic model with
synchronous spikes to disentangle joint pausiness arising from synchronous spikes from additional ‘Joint Excess Pausiness’ (JEP). Parameter estimates are obtained from auto- and cross-correlograms, and statistical
significance is assessed by comparison to simulated cross-pauseograms.
Our new method was applied to dopamine neuron pairs recorded in
the ventral tegmental area of awake behaving mice. Significant JEP
was detected in about 20% of the pairs. Given the neurophysiological importance of pauses and the fact that neurons integrate multiple
inputs, our findings suggest that the analysis of JEP can reveal interesting aspects in the activity of simultaneously recorded neurons.
Acknowledgements
This work was supported by the Priority Program 1665 of the DFG (DU
1433/1-1 to SD and JR, and SCHN 1370/2-1 to MG and GS), by an EMBO
long-term fellowship (ALTF_210-2012 to SD), and by the German Federal Ministry of Education and Research (BMBF, 01ZX1404B to GS).
BMC Neurosci 2017, 18(Suppl 1):60
P233
A stochastic model relates responses to bistable stimuli
to underlying neuronal processes
Stefan Albert1, Katharina Schmack2, Gaby Schneider1
1
Institute of Mathematics, Goethe-University, Frankfurt a.M.,
Germany; 2Department of Psychiatry and Psychotherapy, Charité
Universitätsmedizin, Berlin, Germany
Correspondence: Stefan Albert (albert@math.uni-frankfurt.de)
BMC Neuroscience 2017, 18 (Suppl 1):P233
Viewing of ambiguous stimuli can lead to bistable perception alternating
between the possible percepts. The respective response patterns show
differences between schizophrenic patients and healthy controls [1, 2].
At the same time, these patterns show similarities with spiking patterns
of dopaminergic cells [3] that may be related to schizophrenia spectrum disorders. Specifically, oscillatory behavior [4] with single percept
changes occurs during continuous viewing of ambiguous stimuli, and
stable more or less regular periods followed by bursts of percept changes
are observed during intermittent viewing of ambiguous stimuli.
Therefore, we propose a stochastic model that provides a link between the
observed response patterns and potential underlying neuronal processes.
To that end, we first develop a Hidden Markov Model that captures the
observed group differences by describing switches between stable and
unstable states in the intermittent presentation and using only one state
in continuous presentation. Second, the model is embedded into a hierarchical model that describes potential underlying neuronal activity as difference between two competing neuronal populations similar to [5]. This
differential activity is assumed here to generate switching between (i) the
two conflicting percepts and between (ii) stable and unstable states with
comparable mechanisms on different neuronal levels. Using only a small
number of parameters, the model can be fitted to a large data set of perceptual responses of schizophrenic patients and healthy controls under
continuous and intermittent stimulation. The model can closely reproduce
a wide variety of response patterns and is able to capture and to provide
potential neuronal mechanisms for group differences between healthy
controls and schizophrenic patients such as the weaker tendency to stabilized perception in the patient group under intermittent stimulation [2].
Acknowledgements
This work was supported by the German Federal Ministry of Education
and Research (BMBF, Funding number: 01ZX1404B; SA, KS, GS).
References
1. Schmack K, Gòmez-Carrillo de Castro A, Rothkirch M, Sekutowicz M, Rössler
H, Haynes J, Heinz A, Petrovic P, Sterzer S: Delusions and the Role of Beliefs
in Perceptual Inferences. J Neurosci E 2013, 33(34):13701–13712.
2. Schmack K, Schnack A, Priller J, Sterzer P: Perceptual instability in schizophrenia: Probing predicitive coding accounts of delusions with ambiguous
stimuli. Schizophr Res Cog 2015, 2(2):72–77.
3. Bingmer M, Schiemann J, Roeper J, Schneider G: Measuring burstiness and
regularity in oscillatory spike trains. J Neurosci Methods 2011, 201: 426–437.
4. Brascamp JW, Pearson J, Blake R, van den Berg AV: Intermittent ambiguous
stimuli: Implicit memory causes periodic perceptual alternations. J Vis 2009,
9(3): 1-23.
5. Gigante G, Mattia M, Braun J, Del Guidice P: Bistable perception Modeled
as Competing Stochastic Integration at Two Levels. PLoS Comput Bio 2009,
5(7): e1000430.
P234
Function and energy consumption constrain biophysical properties
of neurons ‑ an example from the auditory brainstem
Michiel Remme1,2, John Rinzel3,4, Susanne Schreiber1,2
1
Institute for Theoretical Biology, Humboldt University, 10115 Berlin,
Germany; 2Bernstein Center for Computational Neuroscience Berlin,
Germany; 3Center for Neural Science, New York University, New York, NY
10003, United States; 4Courant Institute of Mathematical Sciences, New
York University, New York, NY 10012, United States
Correspondence: Michiel Remme (michiel.remme@hu-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P234
Neural morphology and membrane properties vary greatly between
cell types in the nervous system. While the function of neurons is
Page 134 of 176
thought to be the key constraint for their biophysical properties, additional constraints may further shape neuronal design and explain
observed properties. Here, we focus on principal neurons in the MSO
nucleus of the auditory brainstem and show that a tradeoff between
a functionally relevant computation and energy consumption predicts
optimal ranges of biophysical parameters.
Biophysical properties of MSO cells as well as their function are well
characterized: MSO cells encode the direction of sound in the horizontal plane. Inputs to MSO cells are phase-locked to sound wave stimuli
to each ear and the interaural time difference (ITD) of sound waves is
used to compute source location. To achieve sensitivity to ITDs in the
range of tens of μs, MSO cells have specialized membrane properties,
including a very fast membrane time constant (~1 ms) and a lowthreshold potassium current (IKLT ), both contributing to a very short
input integration window [1]. Furthermore, MSO cell function is supported by their bipolar morphology, with inputs from the two ears
segregated to the two main dendrites [2].
Next to function, energy use can be assumed to significantly constrain
MSO cell properties. Overall, the brain accounts for a disproportionately large part (~20%) of the energy budget, with metabolic energy
being mostly spent on synaptic input, action potentials, and resting potentials [3]. MSO cells, in particular, receive inputs at very high
rates (hundreds of Hz), generate action potentials at similarly high
rates, and display a very leaky membrane.
Here, we quantify and contrast sensitivity of MSO cells to ITDs as well
as the associated metabolic cost. We developed a simplified dendritic
model of an MSO cell that includes the KLT-current. We first fit the
model to experimental data from [1] and then explored how varying
the morphological and membrane parameters affects performance
and energy consumption. We found that most experimentally constrained parameters were close to a functional optimum; if a wider
range of functionally good values was available, the fitted parameters
tended towards lower energy usage. Interestingly, we found that the
KLT-current increases energy costs, but strongly improves coincidence
detection, beyond passive capabilities. We next explored the full
parameter space by considering 100,000 models with random combinations of parameters. The experimentally constrained model was
among the top 13% regarding performance and top 12% regarding
energy efficiency (i.e., sensitivity per energy). Exploration of the full
parameter space highlighted that two model features explain most
of their performance and energy consumption: 1) the level of saturation of the driving force of the synaptic conductance inputs and 2) the
width of the somatic compound EPSPs. We conclude that the neural
design of MSO cells is indeed compatible with both functional and
energetic constraints, with a preference of function over cost.
Acknowledgements
This work was supported by the Einstein Foundation Berlin and the
German Federal Ministry of Education and Research (01GQ0901,
01GQ1403).
References
1. Mathews PJ, Jercog PE, Rinzel J, Scott LL, Golding NL. Control of submillisecond synaptic timing in binaural coincidence detectors by Kv1 channels.
Nat Neurosci 2010. 13:601–609.
2. Agmon-Snir H, Carr CE, Rinzel J: The role of dendrites in auditory coincidence detection. Nature 1998, 393:268–272.
3. Attwell D, Laughlin SB: An energy budget for signaling in the grey matter
of the brain. J Cereb Blood Flow Metab 2001, 21:1133–1145.
P235
The Brain Simulation Platform of the Human Brain Project:
collaborative web applications and tools for data‑driven brain
models
Michele Migliore1, Carmen A. Lupascu1, Luca L. Bologna1, Rosanna
Migliore1, Stefano M. Antonel2, Jean-Denis Courcol2, Felix Schürmann2
1
Institute of Biophysics, National Research Council (CNR), Palermo, Italy;
2
Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL),
Geneva, Switzerland
Correspondence: Michele Migliore (michele.migliore@cnr.it)
BMC Neuroscience 2017, 18 (Suppl 1):P235
BMC Neurosci 2017, 18(Suppl 1):60
The Brain Simulation Platform (BSP) of the Human Brain Project (HBP)
provides a large set of tools to build, reconstruct, simulate and analyze
data-driven brain models in a collaborative manner (Figure 1). The
available tools are organized by use cases, consisting of selected procedures illustrating specific practical examples on how to exploit the
Platform capabilities to pursue scientific goals.
The platform is designed to target users with different background
and expertize such as: a) “end-users”, interested in using the platform
in a user-friendly manner, b) “power-users”, able to take advantage of
the platform services while integrating their own High Performance
Computing resources, c) “expert-users”, who can contribute to the
development of the tools, and d) “co-design developers” who are early
adopters of initial versions of the platform facilities.
In this poster, we will give an overview of the current BSP release, the
services it provides and the collaborative approach underlying its
design. To illustrate the potential of the platform, and how users with
different background can take full advantage of its tools, we will demo
a few use cases in which “end-users” and-or “expert-users” are guided
through step-by-step python-based jupyter notebook and web applications graphical interfaces (Figure 1).
Figure 1. The HBP Brain Simulation Platform web interface. A. BSP
Overview web page. B and C. synaptic events Fitting and Electrophysiological Feature Extraction GUIs, developed as a jupyter notebook and a web app respectively
Acknowledgements
This project has received funding from the European Union’s Horizon
2020 research and innovation programme under grant agreement No
720270.
P236
A Single Pyramidal‑Cell and Network Computational Model of the
Hippocampal CA3
Region
Sami Utku Çelikok1, Eva M. Navarro-López2, Neslihan Serap Şengör3
1
Biomedical Engineering Department, Boğaziçi University, Istanbul, 34342,
Turkey; 2School of Computer Science, The University of Manchester,
Manchester, M13 9PL, UK; 3Electronics and Communication Department,
Istanbul Technical University, Istanbul, 34469, Turkey
Correspondence: Sami Utku Çelikok (utku.celikok@boun.edu.tr)
BMC Neuroscience 2017, 18 (Suppl 1):P236
Hippocampal subarea CA3 has long drawn attention for its major role
in encoding spatial representations and episodic memories [1]. Due
to the presence of rich recurrent feedback connections, CA3 has been
considered to play a key role in long-term memory formation. Moreover, CA3 has long been proposed as an auto-associative network capable of pattern completion and path integration for the retrieval and
storage of episodic/declarative memory traces [2]. A broad range of
experimental studies have supported the idea that hippocampal oscillations must be taken into consideration while investigating the region
as a memory network. Empirically-validated studies on freely moving
rats have identified two major oscillatory patterns of hippocampal
Page 135 of 176
activity in a behaviour-dependent context: theta- (4–8 Hz) and
gamma-band (30–100 Hz) frequency rhythms [3, 4]. In rodents and
humans, gamma rhythms embedded into theta oscillations become
prominent during memory functions, object exploration, and spatial
navigation [1]. The consideration of the spiking patterns of the neurons during oscillatory regimes is key to uncover the significance of
hippocampal network oscillations in different processes. When the
broad electrophysiological repertoire of CA3 pyramidal cells is considered, the computational description of the network requires a neural
model. This model has to be simple enough to support a large hippocampal network, but still rich enough to capture complex pyramidal-cell dynamics. This is precisely what we propose here: a single-cell
computational model for a CA3 pyramidal neuron that is used as the
basic element to form a CA3 network model which will be able to
reproduce key hippocampal oscillatory patterns. The spiking patterns
of the offered single-cell model capture some essential features of
well-known hippocampal spiking behaviour, such as: spike broadening at the end of a burst, rebound bursting, low-frequency bursts, and
high-frequency tonic spiking (Figure 1). Moreover, the model for the
CA3 population is also able to generate theta and gamma-band oscillations, known to be present in the CA3 region.
Figure 1. A. Single-cell model results. Upper-left: Initial spike generation, upper-right: rebound bursting in response to hyperpolarisation, bottom: burst-to-tonic spike transition with increased input
current. B. Population model spectrograms. Upper: gamma-band
oscillations in the network, bottom: theta-band oscillations in the
network
References
1. O’Keefe J, Nadel, L: The Hippocampus as a Cognitive Map. Oxford, UK: Oxford
University Press; 1978.
2. Samsonovich A, McNaughton BL: Path integration and cognitive mapping in a continuous attractor neural network model. J Neurosci 1997,
17(15):5900–5920.
3. Gloveli T, Kopell N, Dugladze T: Neuronal activity patterns during hippocampal network oscillations in vitro. In: Hippocampal Microcircuit 2010,
Springer 247–276.
4. Leung LS, Lopes da Silva F, Wadman WJ: Spectral characteristics of the hippocampal EEG in the freely moving rat. Clin Neurophysiol 1982, 54:203–219.
P237
Functional connectivity between prefrontal cortex and striatum
showed by computational model
Rahmi Elibol, Neslihan Serap Sengor
Electronics and Communication Engineering, Istanbul Technical
University, Istanbul, Turkey
Correspondence: Rahmi Elibol (rahmielibol@itu.edu.tr)
BMC Neuroscience 2017, 18 (Suppl 1):P237
It is well-known that there is a strong correlation between cortex and
striatal activity especially during progression of action selection and
goal directed behavior. This interaction between cortex and striatum
project back to the cortex through direct and indirect pathways and
over thalamus forming a closed loop [1]. Such structural associations
BMC Neurosci 2017, 18(Suppl 1):60
of the brain are called structural connectivity or connectome. Due to
the development of measurement technologies as fMRI, more work
has been carried to build up the association between the different
areas of the brain and the cognitive processes, and such associations
are called functional connectivity or functional connectome. Besides
these, the processes carried out at neuronal level and/or the changes
at synaptic connections which give rise to relations that are observed
at frequency and/or phase levels is called dynome [2]. The structural
connection between cortex and striatum is already known and their
functional connectivity has been shown with experimental studies. In
this work, based on the experimental results given in [3], a computational model is proposed based on the dynamical connection of neurons and synapses showing the dynome relation between cortex and
striatum.
During the experimental studies that have been explained in [3], LFP
in prefrontal cortex and striatum are measured. Beta and gamma
frequency bands have been observed and with PLV, the correlation
between cortical and striatal activity has been shown [3, 4]. These
experimental results have been recreated with the computational
model proposed and it is shown that the results given in Figure 1 are
similar to the experimental results. The simulations are carried out by
considering the similar conditions considered in experiments. The
stimuli are applied as in the experimental work and the role of different reward quantities is investigated by changing the dopamine levels.
Figure 1. The correlation between the PFC and striatal activity:
A. The activity in PFC B. The activity in striatum, C. The correlation
between PFC and striatum. The activities in PFC and striatum are
given with normalized firing rate values. The results show the there
is a correlation between cortex and striatum
References
1. GE Alexander, MD Crutcher, MR DeLong, Basal ganglia-thalamocortical
circuits: Parallel substrates for motor, oculomotor, “prefrontal” and “limbic”
functions. Progress in Brain Research, 85, 119–146, http://dx.doi.org/10.1016/
S0079-6123(08)62678-3.
2. NJ Kopell, HJ Gritton, MA Whittington, MA Kramer: Beyond the connectome: the dynome. Neuron 2014, 83(6):1319–1328. doi: 10.1016/j.
neuron.2014.08.016.
3. Y Zhang, X Pan, R Wang, M Sakagami: Functional connectivity between
prefrontal cortex and striatum estimated by phase locking value. Cogn
Neurodyn. 2016, 10(3):245–254. doi: 10.1007/s11571-016-9376-2.
4. EG Antzoulatos, EK Miller: Increases in functional connectivity between
prefrontal cortex and striatum during category learning. Neuron 2014,
83(1):216–225. doi: 10.1016/j.neuron.2014.05.005.
Page 136 of 176
P238
A spiking neural network model of basal ganglia‑thalamocortical
circuit with Brian2
Mustafa Yasir Özdemir, Neslihan Serap Şengör
Electronic-Communication Department, İstanbul Technical University,
İstanbul, Turkey
Correspondence: Mustafa Yasir Özdemir (musyasoz@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P238
Basal ganglia circuit which is located in the midbrain has an essential
role in action selection, decision making and reward based learning
processes. In this work, especially basal ganglia-thalamocortical circuit responsible for motor control giving rise to voluntary movement
is considered.
The characteristics of neuronal activity and their functional abilities,
properties of synaptic connections, effect of neurotransmitters as
dopamine and the relation between different nuclei defined by pathways, all these are effective in realizing voluntary movement. It is long
known that abnormalities in dopamine level influence basal ganglia
operations negatively giving rise to neurological disorders like Parkinson’s Disease, Hungtinton’s chorea, hemiballismus, dystonia [1].
The equations written for neuronal activity are complicated and simulations of computational models are especially versatile to predict the
neuronal activity. Computational models reflect the consequences of
various assumptions made in forming the models [2]. Most computational models of basal ganglia circuits consider a specific process and
only partly reflect their nature and function. In this work, an attempt is
made to obtain a holistic model of basal ganglia-thalamocortical circuit in Brian 2 environment to ease the further improvement and testing of the model by the neuroscientist.
Here a spiking neural network model is realized to configure the entire
properties of basal ganglia circuit. The characteristic neuronal activities of each substructure are obtained by modification of Izhikevich
neuron model [3]. The proposed model of basal ganglia-thalamocortical circuit is also capable of showing the dopamine effect on the processes due to the modified striatum neurons. Medium spiny neurons
which have different dopamine receptors are considered in the model
separately. Also, direct, indirect and hyper-direct pathways exist in the
model and effect of dopamine on these pathways can be observed
in the simulations. Synaptic connections configured to realize learning and probability of connections are set according to the research
presented in the literature. The model is formed with inspiration from
another study [4] and realized on Brian2 simulator.
The simulation results of the model are given by raster plots, firing
rates and time-frequency analysis. The stimulus activity in the cortex
is projected to the thalamus in the simulations and the model reveals
the role of direct, indirect and hyper-direct pathways on the formation
of this projection separately.
References
1. Wichmann T, DeLong MR: Deep Brain Stimulation for Neurologic and Neuropsychiatric Disorders. Neuron 2006, 52(1): 197–204.
2. Schroll H, Hamker FH: Computational models of basal-ganglia pathway
functions: focus on functional neuroanatomy. Frontiers in Sys Neu 2013,
doi:10.3389/fnsys.2013.00122.
3. Izhikevich EM: Which Model to Use for Cortical Spiking Neurons? IEEE Trans
Neural Networks 15:1063–1070.
4. Çelikok U, Navarro-Lopez EM, Şengör NS: A computational model describing the interplay of basal ganglia and subcortical background oscillations
during working memory processes. arXiv:1601.07740
P239
Coordinate‑transformation spiking neural network for spatial
navigation
Tianyi Li, Angelo Arleo, Denis Sheynikhovich
Sorbonne Universités, UPMC Univ Paris 06, INSERM, CNRS, Institut de la
Vision, 17 rue Moreau, 75012 Paris, France
Correspondence: Denis Sheynikhovich (denis.sheynikhovich@upmc.fr)
BMC Neuroscience 2017, 18 (Suppl 1):P239
BMC Neurosci 2017, 18(Suppl 1):60
Spatial navigation in primates is thought to be mediated by neural networks linking the dorsal visual pathway (including parietal and retrosplenial cortices) and the medial temporal lobe [1]. Neurons along this
pathway are sensitive to visual cues of varying complexity (from simple
visual features to views of spatial scenes [2, 3]) and have been characterized to code environmental features in different reference frames (from
egocentric eye- or head-centered representations early in the pathway
to allocentric world-centered ones later in the pathway [3, 4]). However,
neural mechanisms underlying the transformation between egocentricvisual and allocentric-spatial representations remain poorly understood.
In this work, we present a spiking-neural-network model of visuospatial coordinate transformation that receives input in the form of
realistic head-centered visual input with limited view field. After processing this input with V1-like orientation-sensitive neuronal filters, it
is transformed to an allocentric directional frame using two mechanisms, experimentally observed along the dorsal pathway. First, head
direction signal, thought to be provided by the retrosplenial cortex, is
used by the network to align egocentric input views with a world-centered directional frame [4]; Second, short-term visual working memory
in the parietal network serves to link subsequent views during head
rotation into scene-like representation of visual features. The output
of the coordinate-transformation network serves as input to the hippocampus, where location-sensitive neuronal responses are learned
using spike-timing-dependent plasticity.
Neuronal activities in the model are shown to reproduce basic features of dorsal-pathway neurons. In particular, in an experimental
setup mimicking an animal sitting in front of a screen, visual receptive
fields of model parietal/retrosplenial neurons code features in head- or
world-centered reference frames, and firing activities in the transformation network exhibit gain fields with respect to head direction, as
observed in classical experiments with monkeys. In a setup where the
simulated animal explores an experimental environment, modeled
hippocampal cells exhibit location-sensitive firing fields after learning.
These purely visual place fields are influenced by changes in the visuospatial environmental layout (e.g. its spatial geometry [5]), and are
modulated by currently observed view [2]. Moreover, spike synchrony
patterns in this model reflect environment topology [6]. This model
links the processing of low-level visual features in the brain with highlevel cognitive processes implicated in spatial navigation.
Page 137 of 176
neuronal activity to individual cognitive tasks has been studied [1]. In
order to understand the function of the brain more deeply, we need
to investigate the micro-connectome, which is a comprehensive map
of connectivity or interactions of neurons or synapses, beyond the
basic statistical observations of its individual elements [2]. This study
reports the interactions among neurons measured from the anterior
lateral motor cortex (ALM) of mice using calcium fluorescence imaging and focuses on selectivity for cognitive planning of directed licking behaviors [3]. We reconstructed the functional networks from
the spiking activities of the neuron ensembles at resting periods and
compared them with the motion-selectivity of individual neurons
(Figure 1). The network structure was characterized using graph theory [4]. Past studies [3] have declared that significant activities can be
observed in layer 5 of the ALM. However, the contributions of different layers were not reported. Our connectome analyses also consistently showed that, in layer 5 of the ALM, a simple connection strength
measure in motion-selective neurons was significantly stronger than
in motion-nonselective cells. Surprisingly, in layer 2, a Centrality measure was significantly higher in selective cells, especially contralateral
selective cells, than in non-selective cells. Centrality represents that the
cell is in an important position within the network. It has been repeatedly reported that the effective connectivity, the estimated neuronal
activities recorded using Ca Imaging technique in the resting period,
reflects the underlining structural synaptic connectivity fairly well [5].
Therefore, our results suggest that the neurons involved in motorplanning were located at highly central positions in the micro-connectome from the structural design. Because of the position, they will
be able to influence a large number of neuropiles within, and probably beyond, the ALM. If we observe the brain more widely, layer 5
exists on the bottom-up information flow that originally came from
the thalamus, and layer 2 exists on the top-down information flow
relatively close to the output to the thalamus. Therefore, layer 2 in the
micro-connectome may represent a different functional role of the
motor-planning than the neuron group existing in layer 5.
Our
findings and methodological schemes will contribute to a more accurate understanding of cognitive functions, the effects of aging, and
various neurodegenerative diseases.
Acknowledgments
This research was supported by ANR - Essilor SilverSight Chair
ANR-14-CHIN-0001
References
1. Kravitz DJ, Saleem KS, Baker CI, Mishkin M: A new neural framework for
visuospatial processing. Nat Rev Neurosci. 2011, 12:217–230.
2. Ekstrom AD: Why Vision is Important to How We Navigate. Hippocampus
2015, 25:731–735.
3. Snyder LH, Grieve KL, Brotchie P, Andersen R: Separate body- and worldreferenced representations of visual space in parietal cortex. Nature 1998,
394:887–891.
4. Byrne P, Becker S, Burgess N: Remembering the past and imagining the
future: A neural model of spatial memory and imagery. Psychol Rev. 2007,
114:340–375.
5. Sheynikhovich D, Chavarriaga R, Strösslin T, Arleo A, Gerstner W, Strosslin
T, Arleo A, Gerstner W: Is there a geometric module for spatial orientation?
Insights from a rodent navigation model. Psychol Rev. 2009, 116:540–566.
6. Curto C, Itskov V: Cell Groups Reveal Structure of Stimulus Space. PLoS
Comput Biol. 2008, 4:e1000205.
P240
Micro‑connectomics with cognitive task selectivity
Akihiro Nakamura1, Masanori Shimono1,2
1
Osaka University, Toyonaka, Osaka, Japan; 2Riken Brain Science Institute,
Saitama, Japan
Correspondence: Masanori Shimono (smn@bpe.es.osaka-u.ac.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P240
Various cognitive functions of our brain are realized by interactions
among a large number of neurons. Traditionally, the selectivity of
Figure 1. The general concept of this study. A. Neuronal activities
when rodents are taking rest (or just waiting a task) or when performing licking tasks were recorded using Ca Imaging technique.
B. is an example of effective/functional networks of neurons reconstructed from the neuronal dynamics. The differences of markers
show differences of responses of neurons. (Neurons responding
selectively to contralateral lickings (△), to ipsilateral lickings (□),
and neurons showing no responses to these licking behaviors (○))
References
1. Hubel DH, Wiesel TN: Receptive fields and functional architecture of monkey
striate cortex. The Journal of physiology 1968, 195(1): 215–243.
2. Shimono M, Beggs, JM: Functional clusters, hubs, and communities in the
cortical microconnectome. Cerebral Cortex 2015, 25(10): 3743–3757.
BMC Neurosci 2017, 18(Suppl 1):60
Page 138 of 176
3. Li, N, Chen, TW, Guo ZV, Gerfen CR, Svoboda, K.: A motor cortex circuit for
motor planning and movement. Nature 2015, 519(7541): 51–56.
4. Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis
of structural and functional systems. Nature Reviews Neuroscience 2009,
10(3): 186–198.
5. Stetter O, Battaglia D, Soriano J, Geisel T: Model-free reconstruction of
excitatory neuronal connectivity from calcium imaging signals. PLoS Comput Biol 2012, 8(8): e1002
P241
Does reinforcement learning explain zone‑allocation behavior
between two competing mice?
Youngjo Song1, Sol Park1,2, Ilhwan Choi2, Jaeseung Jeong1,3, Hee-sup
Shin2
1
Department of Bio and Brain Engineering, KAIST, Daejeon, 34141,
Republic of Korea; 2Center for Cognition and Sociality, IBS, Daejeon,
34047, Republic of Korea; 3Program of Brain and Cognitive Engineering,
KAIST, Daejeon, 34141, Republic of Korea
Correspondence: Youngjo Song (jsjeong@kaist.ac.kr)
BMC Neuroscience 2017, 18 (Suppl 1):P241
In the previous study (Choi et al., in revision), we observed two mice
showing cooperative-like behavior in the competitive situation over
rewards. We have also shown that this cooperative-like behavior
enhanced mutual rewords and produced payoff equity between two
competing mice. However, the origin of this behavior is not clear. Thus,
the aim of this study is to address whether the cooperative-like behavior could be explained by reinforcement learning or not. In the behavior chamber for mice, two light cues which indicate two reward zones,
respectively. If a mouse goes the left reward zone when the left light
cue turns on, the mouse gets reward, and a mouse can get rewards
if the mouse get in the right reward zone when the right light cue
turns on. The reward is given by wireless brain stimulation from the
electrode implanted in the Medial forebrain bundle (MFB), the pleasure center in the mouse brain. When the mice learned the meaning of
light cues, we performed the pair test in which the two mice released
in one training chamber. In this experiment, 15 out of 19 pairs showed
the tendency to separate and allocate their own reward zone by themselves. In other words, those mice had their own preferred sides and
did not interfere opponent’s preferred side (we called this behavior as
‘zone-allocation behavior’). We followed the ethical guidelines of the
Institutional Animal Care and Use Committee in the KAIST. This behavior could be considered as a heuristic rule of reciprocity and cooperation. To investigate if the reinforcement learning can explain this
behavior in two competing mice, we developed computational model
based on the Temporal difference (TD) learning model. In this computational simulation, the environment is set up identically with the
real training room. The model mouse makes decisions only based on a
state-action value function which is updated by the TD rule. We found
that the computational model successfully mimicked the zone allocating behavior between two model mice. Two types of pairs in our
model were observed. The first type is a pair dividing their own reward
zone each other, which indicates each mouse obtained its own preferred side (Figure 1A). This can be thought as a case of zone allocating
pair in actual experiment. The second type is that one mouse dominates both side of reward zone (Figure 1B). From repetitive iterations,
we obtained 75% of model mouse pairs showing the zone allocating
behavior, which is quite consistent with the experimental results of the
real zone-allocating pair ratio (69%). Moreover, we examined whether
a mouse achieve this behavior when it uses model-based learning. We
used Dyna-Q algorithm to implement this model mouse. Zone allocating behavior, however, could not be achieved. If it uses model-based
learning, it updates its state-action value too often. Therefore, the
mouse’s behavior did not converge.
Figure 1. A. State-action values (Q-value) of a pair of mice showing
zone-allocation behavior. In mouse1, Q-value for R(right) reward
zone is larger than Q-value for L(left) reward zone. It means that
mouse1 prefer R reward zone. In the same way, mouse2 prefer L
reward zone. Moreover, Q-value of mouse1 for L reward zone and
Q-value of mouse2 for R reward zone becomes less than 0.2.
It means that each mouse didn’t interfere opponent’s preferred
side. B. State-action values (Q-value) of a pair of mice not showing zone-allocation behavior. Q-value of mouse2 for reward zone
is converging to zero. It means that mouse2 prefer not to move, so
mouse1 got all the reward
Conclusion: This computational result supports the hypothesis that
the zone allocating rodent behavior can be explained from positive
reinforcement learning (particularly model-free learning). Zone-allocation might be a strategy to maximize reward and to minimize cost in
aspect of reinforcement learning in competitive situation. We suggest
that, to investigate the social heuristic behavior, it might be crucial to
remove convergent egoistic characteristic of animal behavior.
References
1. Richard S. Sutton, Andrew G. Barto:Reinforcement Learning: An Introduction. The MIT press; 1998.
2. Paul W. Glimcher, Ernst Fehr: Neuroeconomics, 2nd Edition. Academic press;
2014
P242
Optimal synaptic scaling emerges from Hebbian learning rules
in balanced networks
Sadra Sadeh1, Padraig Gleeson1, R. Angus Silver1
1
Department of Neuroscience, Physiology and Pharmacology, University
College London, London WC1E 6BT, UK
Correspondence: Sadra Sadeh (s.sadeh@ucl.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P242
Synaptic connectivity varies widely across cell types and brain regions
and connections are formed and lost during development and learning. However, normal function cannot be maintained by simply adding or subtracting excitatory synaptic inputs onto a neuron, since this
will cause neurons to become hyper- or hypo-excitable, resulting in
network instability and loss of function. How then do neurons scale
their synaptic input to maintain function? Theoretical work suggests
that the optimal way of scaling of synaptic weights (J) as the number of synaptic connections per neuron (degree, K) is J ~ 1/√K [1], a
result that has recently been confirmed experimentally [2]. However,
the mechanisms by which such optimal scaling arises are unknown. To
address this question, we implemented Hebbian-like plasticity rules at
excitatory (E) and inhibitory (I) synapses in large-scale balanced spiking networks of primary visual cortex [3]. As K was increased in the
networks we found that synaptic weight decreased with a dependence of J = 1/K0.6, close to the theoretically optimal scaling [1] and
closely matching that found experimentally [2]. Interestingly, optimal
synaptic scaling emerged when Hebbian plasticity was present at both
BMC Neurosci 2017, 18(Suppl 1):60
E and I synapses. In contrast, spiking networks relying solely on plasticity of I → E synapses to balance excitation and inhibition [4] did not
exhibit optimal scaling. A simplified mean-field analysis of network
dynamics explained the dependence of J on K in networks with Hebbian-like plasticity of E and I synapses, while revealing why the optimal
scaling does not always hold in networks with plasticity of only I → E
synapses.
Irrespective of the initial weights and number of synaptic connections, spiking networks with Hebbian-like plasticity of E and I synapses
robustly self-regulated themselves through recurrent inhibition and
learning into a low activity regime where the activity of the E neuronal
population exhibited a long tail of activity. Notably, this was accompanied by higher activity and lower selectivity of I neurons, consistent
with experimental observations. Examination of the input-output relationship of individual current-based or conductance-based neurons
revealed that optimal synaptic scaling robustly preserved neuronal
gain as the number of synaptic inputs was altered. Moreover, contrastinvariant input tuning curves translated to contrast-invariant output
tuning curves only when the optimal (1/√K) scaling of weights was
preserved. Our results thus suggest that Hebbian learning in both E
and I connections is necessary for preserving cortical computation and
function during changes in synaptic connectivity. These findings have
important implications for cortical function during development, and
cortical dysfunction during brain diseases.
Acknowledgements
yFunded by the Wellcome Trust and the ERC.
References
1. van Vreeswijk C, Sompolinsky H: Chaos in neuronal networks with balanced
excitatory and inhibitory activity. Science 1996, 274(5293):1724–1726.
2. Barral J, Reyes AD: Synaptic scaling rule preserves excitatory-inhibitory
balance and salient neuronal network dynamics. Nat Neurosci 2016,
19(12):1690–1696.
3. Sadeh S, Clopath C, Rotter S: Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity. PLoS Comput Biol 2015, 11(6):
e1004307.
4. Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W: Inhibitory Plasticity
Balances Excitation and Inhibition in Sensory Pathways and Memory
Networks. Science 2011, 334(6062):1569–1573.
P243
Deciphering the contributions of oriens‑lacunosum/moleculare
(OLM) cells during local field potential (LFP) theta rhythms in CA1
hippocampus
Alexandra Pierri Chatzikalymniou1,2 Frances K. Skinner1,3,2
1
Krembil Research Institute, University Health Network, Toronto, ON,
Canada; 2Department of Physiology, University of Toronto, Toronto, ON,
Canada; 3Department of Medicine (Neurology), University of Toronto,
Toronto, ON, Canada
Correspondence: Alexandra Pierri Chatzikalymniou (alexandra.chatzikalymniou@mail.utoronto.ca)
BMC Neuroscience 2017, 18 (Suppl 1):P243
In the hippocampus, one of the most prevalent LFP rhythms is the
3–12 Hz “theta” oscillation [1]. This LFP theta rhythm is tightly correlated with spatial navigation, episodic memory and rapid eye movement (REM) sleep [1]. Recent work by Goutagny and colleagues [4]
showed that theta rhythms emerge in the CA1 region of an intact
in vitro hippocampus preparation due to local interactions between
hippocampal interneurons and pyramidal (PYR) cells. Oriens-lacunosum/moleculare (OLM) cells are a major class of GABAergic interneurons in the hippocampus [5]. In addition to inhibiting distal dendrites
of PYR cells in stratum LM, OLM cells disinhibit PYR cells in stratum
radiatum, an inner to middle layer, by inhibiting interneurons that target PYR cells in that region [5].
Our goal is to examine the contributions of OLM cells to ongoing LFP
theta rhythms in the context of the intact in vitro preparation using
computational modeling. We use network models of OLM cells, bistratified cells (BiCs), and basket/axo-axonic cells (BC/AACs) that target PYR cells in specific layers [3], and assess the role of OLM cells as
Page 139 of 176
their interactions with BiCs and the PYR cell vary. We find that the LFP
power is mostly affected by changes in the synaptic conductance from
OLM cells to BiCs rather than by synaptic conductance changes from
BiCs to OLM cells, indicating a more important role for the former. This
observation suggests that progressive inhibition of OLM cells and thus
progressive decrease of their synaptic inputs onto the PYR cell does
not strongly alter LFP characteristics whereas progressive inhibition
of BiCs does. Decomposition of the LFP signal reveals that fluctuations
in power occur due to BiC and BC/AAC synaptic inputs onto the PYR
cell rather than to OLM cell synaptic inputs onto the PYR cell. Selective
removal of either OLM cells or BiCs/BCs/AACs reveal minimal contribution of the OLM cells to the total LFP power across the dendritic tree.
Conversely, the BiCs/BCs/AACs generated LFP component comprises
approximately 90% of the total signal. Furthermore, changes in synaptic weights from OLM cells to the PYR cell do not produce substantial
changes in the LFP.
Brain rhythms can be considered as representations of brain function
[1, 2]. Given that particular inhibitory cell populations and abnormalities in theta rhythms are associated with disease states [2], it is important to understand the cellular contributions to LFP theta rhythm
modulations. Our results show that OLM cells prominently contribute
to local LFP theta through their interactions with other local inhibitory
cell types. Decomposition of the LFP reveals little contribution of synaptic inputs from OLM cells onto the PYR cell. In CA1 PYR cells, distal
and middle apical dendrites comprise two distinct dendritic domains
with separate branching [6]. Since we find that maximum LFP power is
recorded around the soma and the proximal dendrites, OLM cell contributions to LFP theta can be understood in the context of the cytoarchitectonic separation of the of distal and proximal dendrites in PYR
cells which prohibits distal inhibitory inputs from effectively propagating to the soma.
Acknowledgements
Supported by NSERC Canada, Margaret J. Santalo Fellowship (Physiology, Univ Toronto) and SciNet HPC.
References
1. Buzsáki G: Theta oscillations in the hippocampus. Neuron 2002, 33:325–340.
2. Colgin L: Rhythms of the hippocampal network. Nat Neurosci Rev 2016,
17:239–249.
3. Ferguson KA, Huh CYL, Amilhon B, Williams S, Skinner FK: Network models
provide insight into how oriens-lacunosum-moleculare (OLM) and bistratified cell (BSC) interactions influence local CA1 theta rhythms. Front Syst
Neurosci 2015, 9:110.
4. Goutagny R, Jackson J, Williams S: Self-generated theta oscillations in the
hippocampus. Nat Neurosci 2009, 12:1491–1493.
5. Maccaferri G: Stratum oriens horizontal interneurone diversity and hippocampal network dynamics. J Physiol 2005, 562.1:73–80.
6. Spruston N: Pyramidal neurons: dendritic structure and synaptic integration.
Nature Neurosci Rev 2008, 9:206.
P244
Nonlinear optimal control of brain networks
Lazaro M. Sanchez-Rodriguez, Roberto C. Sotero
Hotchkiss Brain Institute and Department of Radiology, University
of Calgary, Calgary, Alberta, Canada, T2 N 1N4
Correspondence: Lazaro M. Sanchez-Rodriguez (lazaro.sanchezrodrgu@
ucalgary.ca)
BMC Neuroscience 2017, 18 (Suppl 1):P244
The problem of controlling brain networks has been the focus of several recent studies given its relationship to brain stimulation. In this
work, we introduce the State-Dependent Ricatti Equation formalism (SDRE) [1] for the computation of optimal control signals in nonlinear brain networks. Firstly, the optimal input for the abatement of
epileptic-like activity in the model proposed in [2] was calculated (see
Figure 1B). Additionally, we looked at higher dimensional systems
consisting of coupled autonomous Duffing oscillators (see Figure 1,
panels C-E). In the linear case our results are in agreement with those
obtained in [3]. However, as the strength of the non-linearity increases,
the fraction of the networks that can be controlled is generally lower
BMC Neurosci 2017, 18(Suppl 1):60
whereas the cost of controlling the systems grows. Thus, we find evidence for supporting the use of realistic nonlinear modeling of electrical neural activity in the design of optimal controllers for brain
networks.
Figure 1. SDRE-optimal control of the networks. A. General
scheme. B. Controlling the model in [2]. As soon as the control
signal (top right corner) is sent, the diseased solution –in red– is
derived to normal background activity. C. Typical trajectory for a
controlled network of autonomous Duffing oscillators coupled
through a scale-free connectivity matrix. Stimuli are inputted
over the nodes with lower degree –third part of the total number
of nodes in the network. D. Expected cost for the control over 25
scale-free networks (N = 100, mean degree ≈ 6). The numbers
over each of the error bars indicate the fraction of the realizations
of the network in which control is achieved as the non-linearity
(coefficient of the cubic term) is changed. For strengths past 125,
none of the networks can be controlled. In this case, the costs are
infinitely high in theory. They are represented as red asterisks at
the top of the panel. E. Analogue to D for randomizations of the
previously computed scale-free networks
References
1. Jayaram A, Tadi M: Synchronization of chaotic systems based on SDRE
method. Chaos Solitons Fractals 2006, 28:707–715.
2. Taylor PN, Thomas J, Sinha N, Dauwels J, Kaiser M, Thesen T, Ruths J: Optimal control based seizure abatement using patient derived connectivity.
Front. Neurosci 2015, 9:1–10.
3. Liu YY, Slotine JJ, Barabási AL: Controllability of complex networks. Nature
2011, 473:167–173.
P245
An inhibitory microcircuit that amplifies the redistribution
of somatic and dendritic inhibition
Loreen Hertäg1, Owen Mackwood1, Henning Sprekeler1
1
Modelling of Cognitive Processes, Berlin Institute of Technology
and Bernstein Center for Computational Neuroscience, Berlin, 10587,
Germany
Correspondence: Loreen Hertäg (loreen.hertaeg@tu-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P245
GABAergic interneurons constitute only a small fraction of neurons
in the brain, but their importance for brain function is undeniable [1].
Moreover, they display a large diversity in their biophysical, physiological and anatomical properties [2], suggesting a functional ‘division of
labor’. However, the computational roles of the various interneuron
types and how they are supported by their individual properties is
largely unknown.
A striking difference between inhibitory cell types is that they form
synapses onto different compartments of their postsynaptic targets.
Parvalbumin- (PV) and somatostatin (SOM)-expressing interneurons, in
particular, seem to predominantly target the perisomatic regions and
the dendrites, respectively. As SOM and PV cells are also connected, it
Page 140 of 176
has been suggested that inhibition can be dynamically redistributed
between the dendrites and somata of pyramidal cells (PCs) [3, 4]. Here,
we argue that a different cortical sub-circuit consisting of SOM- and
vasoactive intestinal peptide (VIP)-expressing interneurons is optimized to control this redistribution by amplifying small top-down control signals.
To support this hypothesis, we performed a mathematical analysis and
simulations of a network model comprising excitatory PCs and inhibitory PV, SOM and VIP neurons. The connectivity in the circuit was chosen according to experimental findings [4]. We show that the SOM-VIP
circuit can serve as an amplifier that translates small top-down signals
onto VIP cells [5, 6] into large changes in the somato-dendritic distribution of inhibition onto PCs. Taken to the extreme, the circuit can
generate winner-take-all (WTA) dynamics that implement a binary
switch for somato-dendritic inhibition.
Furthermore, we interpret key properties of the SOM-VIP sub-circuit in
the light of this hypothesis. We show that the striking lack of recurrent
inhibition as well as the presence of short-term synaptic facilitation
(STF) observed among VIP and SOM cells strengthens the amplification properties of the network. Artificially including recurrent inhibitory connections within the VIP or SOM populations not only weakens
the amplification, but can also lead to pathological conditions in which
almost all cells within each population are silenced. These pathological
states are not observed when firing rate adaptation is included that is,
indeed, a common feature of SOM and VIP neurons.
In summary, our analysis shows that the SOM-VIP sub-circuit is well
suited to redistribute inhibition onto soma and dendrites of excitatory
PC neurons by amplifying small changes in the input signal to VIP cells.
The synaptic and neural properties, including lack of recurrence, presence of STF and firing rate adaptation, underpin this computation by
strengthening the amplification properties and/or avoiding pathological states.
Acknowledgements
The project is funded by the German Federal Ministry for Education
and Research, FKZ 01GQ1201.
References
1. Isaacson, JF, Scanziani M: How inhibition shapes cortical activity. Neuron
2011, 72(2): 231–243.
2. Tremblay, R, Lee, S, and Rudy, B: GABAergic interneurons in the neocortex:
from cellular properties to circuits. Neuron 2016, 91(2): 260–292.
3. Pouille, F, Scanziani, M: Routing of spike series by dynamic circuits in the
hippocampus. Nature 2004, 429(6993): 717–723.
4. Pfeffer, CK, Xue, M, He, M, Huang, ZJ, Scanziani, M: Inhibition of inhibition
in visual cortex: the logic of connections between molecularly distinct
interneurons. Nature neuroscience 2013, 16(8): 1068–1076.
5. Lee, S, Kruglikov, I, Huang, ZJ, Fishell, G, Rudy, B: A disinhibitory circuit mediates motor integration in the somatosensory cortex. Nature neuroscience
2013, 16(11): 1662–1670.
6. Pi, HJ, Hangya, B, Kvitsiani, D, Sanders, JI, Huang, ZJ, Kepecs, A: Cortical
interneurons that specialize in disinhibitory control. Nature 2013, 503(7477),
521–524.
P246
Learning grid cells in recurrent neural networks
Steffen Puhlmann1, Simon N. Weber1,2, Henning Sprekeler1,2
1
MKP, Modelling of cognitive processes, Berlin Institute of Technology,
10587 Berlin, Germany; 2Bernstein Center for Computational
Neuroscience, 10115, Berlin, Germany
Correspondence: Steffen Puhlmann (s.puhlmann@campus.tu-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P246
Grid cells are spatially tuned neurons in the entorhinal cortex, whose
spatial firing fields tessellate the environment with a hexagonal lattice. The mechanisms that underlie this highly symmetric firing pattern
are currently subject to intense debate [1]. As an alternative to attractor and oscillatory interference models that perform path integration
and assume a specific connectivity [1], we recently suggested that
grid cells could be learned in a feedforward network by interacting
excitatory and inhibitory plasticity on spatially modulated inputs [2].
BMC Neurosci 2017, 18(Suppl 1):60
A central prerequisite for the suggested mechanism is that inhibitory
inputs have a broader spatial tuning than their excitatory counterparts. Given that recurrent inhibition is abundant in entorhinal cortex
[3] and spatially tuned [4], we reasoned that this broadened inhibition
could be the result of recurrent processing.
To corroborate this hypothesis, we analyzed a recurrent network
model consisting of excitatory and inhibitory rate neurons. For the
sake of the argument, only the excitatory neurons in the network
receive external, spatially modulated excitatory input. All synapses in
the network are plastic, with Hebbian plasticity on the excitatory synapses and homeostatic plasticity on the inhibitory synapses [5]. When
exposing the network to inputs that mimic the movement of an animal on a linear track, a large fraction of cells in the recurrent network
rapidly develops a grid-like firing pattern. We find that the underlying
mechanism is robust to details of the spatial input tuning and that
the spatial scale of the resulting grids is primarily determined by the
spatial autocorrelation length of inputs. Based on insights from earlier
work on the interaction of excitatory and inhibitory synaptic plasticity [6, 2], we identify key mechanisms in the circuit that are required
for the formation of grid cells: 1) a smooth, saturating nonlinearity in
the interneurons, which ensures that their spatial tuning is broader
than the tuning of their excitatory drive, and 2) sufficiently many and
diverse excitatory inputs to the inhibitory neurons.
Based on these findings, we suggest that grid cells could be bootstrapped from a large variety of spatially modulated excitatory inputs
to a recurrent network of excitatory and inhibitory neurons with synaptic plasticity on all synapses.
Acknowledgments
The project is funded by the German Federal Ministry for Education
and Research, FKZ 01GQ1201.
References
1. Giocomo LM, Moser MB, Moser EI: Computational models of grid cells.
Neuron 2011, 71(4):589–603.
2. Weber SN, Sprekeler H: Learning place cells, grid cells and invariances: A
unifying model. bioRxiv 2017, 102525.
3. Couey JJ, Witoelar A, Zhang SJ, Zheng K, Ye J, Dunn B, Czajkowski R, Moser
MB, Moser EI, Roudi Y, et al.: Recurrent inhibitory circuitry as a mechanism
for grid formation. Nat Neurosci 2013, 16(3):318–324.
4. Buetfering C, Allen K, Monyer H: Parvalbumin interneurons provide grid
cell-driven recurrent inhibition in the medial entorhinal cortex. Nat Neurosci 2014, 15(5):710–718.
5. Vogels TP, Sprekeler H, Zenke F, Clopath C, Gerstner W: Inhibitory plasticity
balances excitation and inhibition in sensory pathways and memory
networks. Science 2011, 334(6062):1569–1573.
6. Clopath C, Vogels TP, Froemke RC, Sprekeler H: Receptive field formation
by interacting excitatory and inhibitory synaptic plasticity. bioRxiv 2016,
066589.
P247
A model of perceptual learning, biases, and roving
David Higgins1,2, Henning Sprekeler1,2
1
Modelling of Cognitive Processes, TU Berlin, 10587, Germany; 2Bernstein
Center for Computational Neuroscience, Berlin, 10115, Germany
Correspondence: David Higgins (dave@uiginn.com)
BMC Neuroscience 2017, 18 (Suppl 1):P247
Roving is a random task-sequencing paradigm, in perceptual learning, whereby multiple tasks are learned in a randomly interleaved
sequence. For certain experiments, such as bisection tasks, human
subjects appear to be unable to learn the individual tasks under roving conditions [1]. In general, theoretical descriptions of perceptual
learning experiments have resorted to approaches involving tuning
of inputs, using either recurrence or suppression [2, 3]. However, these
approaches have exhibited only partial success in tackling roving. In
2012, Herzog et al. [4] proposed a theoretically inspired explanation
involving a constant drift in synaptic efficacies in the system (unsupervised bias), due to an inability to maintain accurate task specific estimates of performance. This leads to a failure to learn using feedback.
We update this approach, adding additional features, which though
Page 141 of 176
adding realism tend to counteract the action of the unsupervised bias.
We then use this model to examine whether the unsupervised bias is
sufficient to explain roving or not.
The proof-of-concept model proposed in Herzog et al. [4] does
indeed lead to a failure to correctly learn during roving but, while it
fails due to the mooted unsupervised bias in the learning rule, the
implementation relies on unbounded weight growth, an unrealistic
phenomenon. We introduce a simple weight normalisation term, to
counteract the unbounded weight growth, and implement a cognitive bias, often observed in human subjects, towards 50:50 presentation ratios. We thus discover a more appropriate model of human
perceptual learning performance. Our model (i) learns correctly on
a single bisection or vernier task, (ii) fails to learn during roving of
multiple tasks, (iii) exhibits the human tendency towards 50:50 ratios
of choice, thus failing when a 75:25 ratio is used, and (iv) correctly
learns when informed of the altered presentation ratio, similarly to
human subjects (unpublished data). A further extension to the original model, operating on a much slower timescale, allows the task
critic system to learn over time to separately identify the tasks. This
ultimately leads to learning of the initially unlearnable tasks, as seen
in [5].
Our model can be seen as the distillation of the mechanism of failure to learn due to the unsupervised bias. Consistent with intuitions
within the perceptual learning community, our model indicates that
the degree of overlap in task representations, combined with the
unsupervised bias, leads to the difference in outcomes between successful transfer learning versus failure. Interestingly, a cognitive bias in
the task presentation ratio appears to be quite helpful in a range of
presentation paradigms, often counteracting the unsupervised bias
and rescuing potential failures to learn correctly. Our work would combine quite well with the more detailed work of Liu et al. [6] to provide a
full model of perceptual learning in the visual system.
References
1. -Otto, TU, Herzog MH, Fahle M, Zhaoping L: Perceptual Learning with
Spatial Uncertainties. Vision Research 2006, 46(19): 3223–3233.
2. Zhaoping, L, Herzog MH, Dayan P: Nonlinear Ideal Observation and Recurrent Preprocessing in Perceptual Learning. Network 2003, 14(2): 233–247.
3. Schäfer, R, Vasilaki E, Senn W: Adaptive Gain Modulation in V1 Explains Contextual Modifications during Bisection Learning. PLOS Comput Biol 2009,
5(12): e1000617.
4. Herzog, MH, Aberg KC, Frémaux N, Gerstner W, Sprekeler H: Perceptual
Learning, Roving and the Unsupervised Bias. Vision Research 2012, 61:
95–99.
5. Parkosadze K, Otto TU, Malania M, Kezeli A, Herzog M: Perceptual Learning
of Bisection Stimuli under Roving: Slow and Largely Specific. Journal of
Vision 2008, 8(1): 5.
6. Liu J, Dosher BA, Lu ZL: Augmented Hebbian Reweighting Accounts for
Accuracy and Induced Bias in Perceptual Learning with Reverse Feedback.
Journal of Vision 2015, 15(10): 10–10.
P248
Presynaptic inhibition provides a rapid stabilization of recurrent
excitation in the face of plasticity
Laura B. Naumann1,2, Henning Sprekeler1,2
1
Modelling of Cognitive Processes, Berlin Institute of Technology, Berlin,
Germany; 2Bernstein Center for Computational Neuroscience, Berlin,
Germany
Correspondence: Laura B. Naumann (laura-bella.naumann@bccn-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P248
Synaptic plasticity in recurrent neural networks is believed to underlie learning and memory in the brain. One practical problem of this
hypothesis is that recurrent excitation forms a positive feedback loop
that can easily be destabilized by synaptic plasticity. Numerous homeostatic mechanisms have been suggested to stabilize plastic recurrent networks [1], but recent computational work indicates that all
these mechanisms share a major caveat: An effective rate stabilization
requires a homeostatic process that operates on the order of seconds,
while experimentally observed mechanisms such as synaptic scaling
occur over much longer timescales [2].
BMC Neurosci 2017, 18(Suppl 1):60
Here, we suggest presynaptic inhibition as an alternative homeostatic
process, which does not suffer from this discrepancy in timescales.
Experimental studies have revealed that excess network activity can
trigger an inhibition of transmitter release at excitatory synapses
through the activation of presynaptic GABAB receptors, which effectively weakens synaptic strength [3]. This attenuation of recurrent
interactions has been observed to be fully reversible and acts on timescales of 100 s of milliseconds, thus constituting a candidate mechanism for the rapid compensation of synaptic changes.
To highlight the beneficial properties of presynaptic inhibition in excitatory recurrent circuits, we analyzed a simple rate-based recurrent
network model. Presynaptic inhibition is mimicked by multiplicatively
scaling down recurrent excitatory weights in response to excess population activity. Using analytical and numerical methods, we show that
presynaptic inhibition ensures a gradual increase of firing rates with
growing recurrent excitation, even for very strong recurrence (Fig. 1A).
An in-depth mathematical analysis of the underlying dynamical system further reveals that the stability of non-zero fixed points (Fig 1A,
filled markers) is largely independent of model parameters. In contrast,
classical subtractive postsynaptic inhibition is unable to control recurrent excitation once it has surpassed a critical value (Fig. 1B). Moreover,
we investigate the conditions under which presynaptic inhibition can
stabilize recurrent networks if Hebbian assemblies are imprinted.
In summary, the multiplicative character of presynaptic inhibition provides a powerful homeostatic mechanism to rapidly reduce effective
recurrent interactions while retaining synaptic weights and hence conserving the underlying connectivity. It might therefore set the stage
for stable learning without interfering with plasticity at the level of
single synapses.
Figure 1. Steady state firing rates as a function of recurrent
strength for different input intensities Iext. A. presynaptic inhibition
B. postsynaptic inhibition
References
1. Abbott LF, Nelson SB: Synaptic plasticity: taming the beast. Nat Neurosci
2000, 3:1178–1490.
2. Zenke F, Gerstner W: Hebbian plasticity requires compensatory processes
on multiple timescales. Phil Trans R Soc B 2017, 372(1715):20160259.
3. Urban-Ciecko J, Fanselow EE, Barth AL: Neocortical Somatostatin Neurons
Reversibly Silence Excitatory Transmission via GABAb Receptors. Curr Biol
2016, 25(6):722–731.
P249
A grid score for individual spikes of grid cells
Simon N. Weber1,2, Henning Sprekeler1,2
1
Berlin Institute of Technology, 10587 Berlin, Germany; 2Bernstein Center
for Computational Neuroscience, 10115 Berlin, Germany
Correspondence: Simon N. Weber (weber@tu-berlin.de)
BMC Neuroscience 2017, 18 (Suppl 1):P249
The location-specific firing of cells in the entorhinal cortex is subject to
extensive experimental and theoretical research. When classifying the
tuning properties of entorhinal cells, researchers distinguish between
grid cells, i.e., cells whose firing locations form a hexagonal grid, and cells
that fire periodically but without hexagonal symmetry [1–3]. This classification requires a measure for the symmetry of spatially modulated firing
patterns — a grid score. The most established grid score is computed in
multiple stages [e.g., 4]. Spike locations are transformed into a rate map.
Page 142 of 176
Subsequently, an autocorrelogram of the rate map is cropped, rotated
and correlated with its unrotated copy. The final grid score is obtained
from the resulting correlation-vs-angle function at selected angles. This
procedure results in a global grid score for the firing pattern, whose exact
value depends on the parameter choices required at each stage.
Here we suggest a new approach that computes a local grid score —
and the local grid orientation — for each individual spike, directly from
spike locations. We compare it to established grid scores and show that
it is at least as reliable in quantifying the global grid score of the spike
pattern and robust to noise on the spike locations. The score enables
the plotting of spike locations, color coded with the local grid score or
the local orientation of the grid and could thus simplify the visualization of experimental data. More specifically, it could be used to quantify
and highlight recent experimental findings, like boundary effects on
the structure of grids in asymmetric enclosures [5], drifts in grid orientation along the arena [6] or the preferred alignment of grids to one of
the boundaries [6]. The grid score is applicable to any n-fold symmetry.
We provide a public Python package (using SciPy and NumPy) that
efficiently determines the grid score directly from spike locations.
Acknowledgements
Funded by the German Federal Ministry for Education and Research,
FKZ 01GQ1201.
References
1. Krupic J, Burgess N, O’Keefe J: Neural representations of location composed
of spatially periodic bands. Science 2012, 337(6096):853–857.
2. Buetfering C, Allen K, Monyer H: Parvalbumin interneurons provide grid
cell-driven recurrent inhibition in the medial entorhinal cortex. Nature
Neurosci 2014, 17(5):710–718.
3. Kropff E, Carmichael JE, Moser MB, Moser EI. Speed cells in the medial
entorhinal cortex. Nature 2015, 523(7561), 419–424.
4. Sargolini F, Fyhn M, Hafting T, McNaughton BL, Witter MP, Moser MB,
Moser EI: Conjunctive representation of position, direction, and velocity in
entorhinal cortex. Science 2016, 312(5774), 758–762.
5. Krupic J, Bauza M, Burton S, Barry C, O’Keefe J: Grid cell symmetry is shaped
by environmental geometry. Nature 2015, 518(7538), 232–235.
6. Stensola T, Stensola H, Moser MB, Moser EI: Shearing-induced asymmetry in
entorhinal grid cells. Nature 2015, 518(7538), 207–212.
P250
Cortical circuits implement optimal integration of context
Ramakrisnan Iyer, Stefan Mihalas
Allen Institute for Brain Science, Seattle, WA, 98109, USA
Correspondence: Stefan Mihalas (stefanm@alleninstitute.org)
BMC Neuroscience 2017, 18 (Suppl 1):P250
Neurons in the primary visual cortex (V1) predominantly respond to a
patch of the visual input, their classical receptive field. These responses
are modulated by the visual input in the surround [1]. This reflects the
fact that features in natural scenes do not occur in isolation: lines, surfaces are generally continuous, and the surround provides context for
the information in the classical receptive field. It is generally assumed
that the information in the near surround is transmitted via lateral connections, between neurons in the same area [1]. A series of large scale
efforts have recently described the relation between the lateral connectivity and visual evoked responses and found like-to-like connectivity
between excitatory neurons [2, 3]. Additionally, specific cell type connectivity for inhibitory neuron types has been described [4]. However
current normative models of cortical function rely on sparsity [5], saliency [6] predict functional inhibition between similarly tuned neurons.
What computations are consistent with the observed structure of the
lateral connections between the excitatory and diverse types of inhibitory neurons? We combined natural scene statistics [7] and mouse V1
neuron responses [8] to compute the lateral connections and computations of individual neurons which would optimally integrate information from the classical receptive field with that from the surround. The
direct implementation requires single neurons to make complex computations on their inputs. While it is possible for such computations to
be implemented by the dendritic trees, we show that an approximation can be achieved with relatively simple neurons. We show that this
BMC Neurosci 2017, 18(Suppl 1):60
network has “like-to-like” lateral connections between excitatory neurons similar to the observed one [2, 3], distance dependence of connections similar to the observed ones [9], and requires three classes of
inhibitory neurons: one performing local normalization, one surround
inhibition, and one gating the inhibition from the surround, similar to
anatomical [4] and physiological studies. This method generates an
entire connectivity matrix for lateral connections in a layer in a purely
unsupervised fashion, such that it generates testable hypotheses for
connectome studies. Additionally, when these lateral connections
are implemented in a neuronal network the reconstruction of natural
scenes is significantly improved. For images with different statistics,
such as independent and identically distributed random patches, using
a natural scene prior hurts reconstruction. However, an additional gating mechanism allows optimal reconstruction for this type of features
as well. We hypothesize that this computation: optimal integration of
contextual cues is a general property of cortical circuits, and the rules
constructed for mouse V1 generalize to other areas and species.
References
1. Angelucci A. and Bressloff P. C. Contribution of feedforward, lateral and
feedback connections to the classical receptive field center and extraclassical receptive field surround of primate V1 neurons. In Progress in brain
research 2006, 154:93–120.
2. Ko H., B Hofer S. B., Pichler B., Buchanan K. A., Sjöström P. J., and Mrsic-Flogel
T. D. Functional specificity of local synaptic connections in neocortical
networks. Nature, 473(7345):87–91, 5 2011.
3. Lee WC., Bonin V., Reed M., Graham B. J., Hood G., Glattfelder K., and Reid
R.C. Anatomy and function of an excitatory network in the visual cortex.
Nature, 532(7599):370–4, 4 2016.
4. Jiang X., Shen S., Cadwell C. R., Berens P., Sinz F., Ecker A. S., Patel S., and
Tolias A. S. Principles of connectivity among morphologically defined cell
types in adult neocortex. Science, 350, 11 2015.
5. Olshausen B. A. and Field D. J. Emergence of simple-cell receptive
field properties by learning a sparse code for natural images. Nature,
381(6583):607–9, 6 1996.
6. Coen-Cagli R., Dayan P., and Schwartz O. Cortical Surround Interactions and
Perceptual Salience via Natural Scene Statistics. PLoS computational biology, 8(3):e1002405, 3 2012.
7. Martin D, Fowlkes C, Tal D, and Malik J. A Database of Human Segmented
Natural Images and its Application to Evaluating Segmentation Algorithms
and Measuring Ecological Statistics. Proc. 8th Int’l Conf. Computer Vision 2
416–423 2001
8. Durand S., Iyer R., Mizuseki K., De Vries S., Mihalas S., and Reid R.C. A comparison of visual response properties in the lateral geniculate nucleus and
primary visual cortex of awake and anesthetized mice. Journal of Neuroscience, 36(48), 2016.
9. Levy R. B. and Reyes A. D. Spatial Profile of Excitatory and Inhibitory Synaptic
Connectivity in Mouse Primary Auditory Cortex. Journal of Neuroscience,
32(16), 2012.
P251
Neural cross‑frequency coupling functions in the resting state
with eyes open and eyes closed
Valentina Ticcinelli1, Tomislav Stankovski1,2, Peter V. E. McClintock1
and Aneta Stefanovska1
1
Department of Physics, Lancaster University, Lancaster LA1 4YB, United
Kingdom; 2Faculty of Medicine, Ss Cyril and Methodius University, Skopje
1000, Macedonia
Correspondence: Valentina Ticcinelli (v.ticcinelli@lancaster.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P251
The electrophysiological activity of the brain emerges from interactions between large-scale neuronal ensembles [1], and is regulated by
different types of cross-frequency coupling [2–5]. The latter are usually
characterised by their coupling strength and directionality [2–4]. However, it is also possible to investigate the functional mechanisms of the
interaction [5]. We introduce dynamical Bayesian inference for estimation of the coupling functions of neural oscillations in the presence of
noise [6–8]. All of the possible phase-to-phase interactions between
the oscillators of the network are inferred. Thus, the coupling can be
decomposed into its partial functional contributions [8]. This allows one
Page 143 of 176
e.g. to isolate the estimated direct coupling between two nodes from
the possible common coupling and self-coupling also involved in the
interaction. As an illustrative example, the method is applied to characterization of the phase-to-phase neural coupling functions in electroencephalographic (EEG) data from the Neurophysiological Biomarker
Toolbox (NBT) dataset [9]. Comparisons are made between the resting
states with the eyes open (EO) and eyes closed (EC). We constructed the
network by investigating the couplings between delta and alpha waves
extracted from any pair of probes within the 10–20 measuring system;
and we used phase-shuffled surrogates to test the significance of the
inferred direct coupling strength. In doing so, we confirmed the earlier
observation that the direct coupling is stronger in the EC state [10].
By investigating the form of the coupling functions, we were able to
evaluate both inter-subject and intra-subject variability. We also evaluated the time variability of the form of the coupling. We showed that
the coupling function is significantly less variable for the EC state. In a
wider context, the method could in principle be applied to any pair of
coupled oscillations in the same way as in the example shown here.
Acknowledgements
This work was supported by the Engineering and Physical Sciences
Research Council (UK) [Grant No.EP/100999X1], by the EU projects
BRACCIA [517133] and COSMOS [642563], and by the Action Medical Research (UK) project MASDA [GN1963]. VT is supported by a PhD
grant from the Department of Physics, Lancaster University. Our grateful thanks are due to Klaus Lehnertz and Andreas Daffertshofer for valuable discussions. We also thank Lall Hussain for pointing out the toolbox
and for his initial contribution to the work, and Lars Michels and the NBT
research team for sharing their data and for most useful comments.
References
1. Breakspear, M., Heitmann, S., and Daffertshofer, A.: Generative models of
cortical oscillations: neurobiological implications of the Kuramoto model,
Front. Human Neurosci. 2010, 4:190.
2. Friston, K. J., Harrison, L., and Penny, W.: Dynamic causal modelling, Neuroimage 2003, 19: 1273–1302
3. Jensen, O. and Colgin, L. L.: Cross-frequency coupling between neuronal
oscillations, Trends Cognit. Sci. 2007, 11: 267–269.
4. Varela, F., Lachaux, J.-P., Rodriguez, E., and Martinerie, J. (). The brainweb:
phase synchronization and large-scale integration, Nat. Rev. Neurosci. 2001,
2: 229–239.
5. Kralemann, B., Cimponeriu, L., Rosenblum, M., Pikovsky, A., and Mrowka, R.:
Phase dynamics of coupled oscillators reconstructed from data, Phys. Rev. E
2008, 77: 066205.
6. Stankovski, T., Duggento, A., McClintock, P. V. E., and Stefanovska, A.: Inference of time-evolving coupled dynamical systems in the presence of
noise, Phys. Rev. Lett. 2012, 109: 024101.
7. Stankovski, T., Duggento, A., McClintock, P. V., and Stefanovska, A.: A tutorial
on time-evolving dynamical Bayesian inference, The European Physical
Journal Special Topics 2014, 223.13: 2685–2703.
8. Stankovski, T., Ticcinelli, V., McClintock, P. V. E., and Stefanovska, A.: Coupling
functions in networks of oscillators, New J. Phys. 2015, 17: 035002.
9. Neurophysiological Biomarker Toolbox [https://www.nbtwiki.net/]
10. Deco, G., Jirsa, V. K., and McIntosh, A. R.: Emerging concepts for the dynamical organization of resting-state activity in the brain, Nat. Rev. Neurosci.
2010, 12: 43–56.
P252
Dissecting the total astrocytic potassium current in a computational
model
Predrag Janjić1, Dimitar Solev3, Gerald Seifert2, Ljupčo Kocarev1, Christian
Steinhäuser2
1
Laboratory for Complex Systems and Networks, Macedonian
Academy of Sciences and Arts, Skopje, Macedonia; 2Institute of Cellular
Neurosciences, University of Bonn Medical School, Bonn, Germany;
3
Unaffiliated, dimitar.solev@gmail.com
Correspondence: Predrag Janjić (predrag.a.janjic@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P252
Despite the growing experimental evidence about composition of the
K+ conductance in mammalian astroglia [1], the origin of its typical
BMC Neurosci 2017, 18(Suppl 1):60
linear whole cell I/V relation has not been addressed in detail with a
computational model. We have used data of pharmacologically isolated Kir4.1, K2P and Kv4 K+ currents in freshly isolated astrocytes from
mouse hippocampus [2] to describe mathematically equilibrium I/V
characteristics and activation kinetics where applicable, for each current component separately. To account in more detail for the notable
outward current in weakly rectifying Kir4.1 channels, we propose an
extension of the Hagiwara model of dominant inwardly-rectifying
Kir current, by adding a residual outward component. Allowing for a
separate outward Kir current component, additive to the standard
Boltzman equation we achieved a much better fit of the Ba2+sensitive
current (Fig. 1). Assuming a short-pore structure of Kir4.1 channels
we describe the outward voltage- and concentration-dependence
of Kir4.1 permeability using the transition-state theory of the Eyring
reaction-rate formalism, considering that the Mg2+block shapes the
permeability of weak rectifier channels. Our extended model exposes
several parameters whose ranges could be more precisely estimated
by molecular dynamics simulation studies, or with single channel
measurements in targeted point-mutation studies. In addition to
describing the steady-state voltage dependence of K2P currents (represented in our case mostly by currents through TREK-1 and TREK-2
channels) its activation kinetics has been estimated, which smoothed
out the contribution of this current component in differential models
of K+ homeostasis or other dynamic phenomena. Inactivating 4-AP
sensitive currents through Kv4 channels which are expressed at low
density by astrocytes, have been modeled using standard HodgkinHuxley model. Added up, all three modeled current components successfully described the voltage-dependence of total experimental
whole-cell K+ currents. We exemplified the usefulness of our model
by simulating astrocytic currents in elevated K+ concentration in a
single ECS pocket apposing the glial membrane. We believe that such
a detailed model, which separately describes the individual current
components, could be useful in describing the impact of channelopathies underlying altered astrocytic electrophysiology.
Figure 1. Total Ba2+sensitive current through Kir4.1 channels in
an isolated astrocyte (circles) has been initially fitted by the Hagiwara model to describe the inwardly-rectifying part (blue line,
extending to -120 mV). Residual outward Kir current (red triangles)
obtained after subtracting inward current from the control, has
been fitted with a 1-site, 2-barrier reaction rate model (red line).
Added together (black line) they represent the total fit of Kir4.1
current. Both model components describe voltage- and concentration-dependence. External K+ concentration has been elevated to
[K]o = 5 mM in all measurements, with [K]i = 130 mM. Numerical fit
has been limited to -120 mV due to distortions from external Na+
block for very negative voltages, beyond the physiological voltage
range.
Page 144 of 176
References
1. Seifert G, Henneberger C, Steinhäuser C: Diversity of astrocyte potassium
channels - An update, Brain Res Rev 2017, in-press, available online at:
http://dx.doi.org/10.1016/j.brainresbull.2016.12.002
2. Seifert G, Hüttmann K, Binder DK, Hartmann C, Wyczynski A, Neusch C,
Steinhäuser C: Analysis of Astroglial K+ Channel Expression in the Developing Hippocampus Reveals a Predominant Role of the Kir4.1 Subunit, J.
Neurosci 2009; 29 (23): 7474–7488.
P253
Information rate of multiple synaptic release sites with separately
released vesicles during short‑term depression
Mehrdad Salmasi1,2,3, Stefan Glasauer1,2,3,4, Martin Stemmler2,5
1
Graduate School of Systemic Neurosciences, Ludwig-Maximilian
University, Munich, Germany; 2Bernstein Center for Computational
Neuroscience, Munich, Germany; 3German Center for Vertigo and Balance
Disorders, Ludwig-Maximilian University, Munich, Germany; 4Department
of Neurology, Ludwig-Maximilian University, Munich, Germany;
5
Department of Biology II, Ludwig-Maximilian University, Munich, Germany
Correspondence: Mehrdad Salmasi (mehrdad.salmasi@lrz.
uni-muenchen.de)
BMC Neuroscience 2017, 18 (Suppl 1):P253
Chemical synapses are conduits for much of the brain’s information,
but they are inherently unreliable, with unprovoked, spontaneous
release of neurotransmitter alternating with intermittent unresponsiveness of the synapse to action potentials. A release of vesicle at a
synapse depresses the probability of further releases in the short term.
Given the stochastic nature of synapses, synaptic information efficacy
has been used to quantify information transmission through synapses
[1, 2]. Many theoretical approaches treat the synapse as a static, monolithic communication device. Yet many synapses have multiple release
sites, each subject to separate short-term dynamics. These multiple
sites compensate for the unreliability of individual release sites. Here
we seek to quantify how the number of release sites affects the information efficacy of a synapse with short-term depression. In addition,
we study the trade-off between the reliability of information transmission and energy consumption at the synapse.
To analyze the amount of information that a neuron can transfer
through its release sites during short-term depression (Fig. 1A), we
model each release site as a binary asymmetric channel whose state
(release probability) is determined by its release history. A Markov
chain of state transitions implements short-term depression followed
by exponential recovery of the release site. It is assumed that the
release sites are independent and the released vesicles are separable.
We prove that the mutual information rate between the input spike
process, X, and the release outcomes of the release sites, (Y1, Y2,…, YK),
is equal to the statistical average over the information rates of an equivalent communication channel for every possible state combination of
the release sites. Using the derived expression, we show the compensatory effect of having multiple release sites in Fig. 1B. The dashed black
line connects the capacity values of the neuron for different number of
release sites. For a neuron with larger number of release sites, capacity
is achieved at higher input spike rates. We then normalize the information rate by the energy consumed for the release and assess the compromise between the energy and the information rate of the neuron.
Figure 1. A. Information transmission through multiple release
sites of a neuron. B. Mutual information rate of the neuron’s release
site(s) as a function of normalized input spike rate, for different
numbers of release sites. The normalized spike rate is the probability of having a spike in a time bin, and is equal to one when there is
one spike per time bin
BMC Neurosci 2017, 18(Suppl 1):60
Acknowledgements
This work was supported by the BMBF grant 01EO1401 (German
Center for Vertigo and Balance Disorders).
Page 145 of 176
Acknowledgements
This work is supported by Air Force grant FA9550-15-1-0398 and NSF
grant IIS-1526642
References
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1. London M, Schreibman A, Häusser M, Larkum ME, Segev I: The information
efficacy of a synapse. Nature Neuroscience 2002, 5(4), 332-340.
2. Fuhrmann G, Segev I, Markram H, Tsodyks M: Coding of temporal information by activity-dependent synapses. Journal of Neurophysiology 2002,
87(1), 140–148.
1. Brunel N, Hakim V, Isope P, Nadal JP, Barbour B: Optimal information storage
and the distribution of synaptic weights: perceptron versus Purkinje cell.
Neuron 2004, 43(5):745-757.
2. Chapeton J, Gala R, Stepanyants A: Effects of homeostatic constraints on
associative memory storage and synaptic connectivity of cortical circuits.
Frontiers in computational neuroscience 2015, 9:74.
3. Chapeton J, Fares T, LaSota D, Stepanyants A: Efficient associative memory
storage in cortical circuits of inhibitory and excitatory neurons. Proc Natl
Acad Sci U S A 2012, 109(51):E3614–3622.
4. Perin R, Berger TK, Markram H: A synaptic organizing principle for cortical
neuronal groups. Proc Natl Acad Sci U S A 2011, 108(13):5419–5424.
P254
Properties of recurrent networks at maximum capacity for storing
sequences of network states
Danke Zhang, Chi Zhang, Armen Stepanyants
Department of Physics and Center for Interdisciplinary Research
on Complex Systems, Northeastern University, Boston, MA 02115, USA
Correspondence: Armen Stepanyants (a.stepanyants@neu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P254
The ability of neural networks to associate successive states of network activity lies at the basis of various cognitive functions. In this
study, we hypothesized that ubiquitous features of cortical network
structure and dynamics develop as a result of continual memory storage. To test this hypothesis, we consider recurrent McCulloch and Pitts
networks in which neurons may belong to different classes defined by
their excitatory or inhibitory nature, firing probability, and robustness
to noise. Learning in the network is mediated by changes in connection weights in the presence of constraints on the l1-norms of presynaptic weights of individual neurons.
To determine the memory storage capacity of the network we train
the network on a set of random sequences of different lengths and
subsequently test the retrieval of learned memories. The maximum
(critical) capacity of the network is defined as the sequence length for
which the success rate in memory retrieval equals 0.5. To retrieve a
learned sequence, we initialize the network state at the beginning
of the sequence and monitor memory playout. The sequence is considered to be retrieved successfully if the network states during the
retrieval do not deviate substantially from the learned sequence. In
practice, there is no need to precisely define the threshold amount
of deviation. This is because for large networks, e.g. N > 100 neurons,
the Hamming distance between the final states of the learned and
retrieved sequences either remains within ~N0.5, or diverges to ~N.
Memory retrieval in the former case is said to be successful, while in
the latter case the memory could not be retrieved.
We performed numerical simulations for networks of N = 1000 neurons and also solved the problem theoretically in the thermodynamic
limit by using the replica theory [1–3]. The results show that critical networks have unique structural and dynamic properties which
resemble those observed in many cortical systems from cerebellum
to neocortex to hippocampus. First, we find that, consistent with the
experimental data, probability of inhibitory connections in critical
networks is greater than 0.5, whereas excitatory connectivity is sparse
with connection probabilities less than 0.5. Second, we compare the
distributions of connection weights in critical networks with the distributions of amplitudes of excitatory and inhibitory postsynaptic
potentials. Due to the presence of very strong connections, the latter
distributions typically have long, super-exponential tails. We show that
in critical networks this feature can result from the heterogeneity of
properties of individual neurons. Third, we find that with increasing
robustness, critical networks exhibit a phase transition from networks
with ordered dynamics quickly terminating in a frozen state, to networks with chaotic dynamics during which neurons exhibit irregular
and correlated firing activity with average correlation coefficients
in the 0.1–0.2 range. Finally, we show that the observed transition is
accompanied with the emergence of neuron clusters, existence of
which is suggested by recent experimental studies [4]. These results
are consistent with the idea that cortical networks are operating in a
critical state configured at the edge of order-to-chaos phase transition.
P255
Modulation of epileptic activity in thalamo‑cortical networks
by input from the cerebellar nuclei
Julia Goncharenko1, Lieke Kros2, Neil Davey1, Christoph Metzner1, Chris de
Zeeuw2, Freek Hoebeek2, Volker Steuber1
1
Centre for Computer Science and Informatics Research, University
of Hertfordshire, Hatfield, AL10 9AB, UK; 2Department of Neuroscience,
Erasmus MC, Wytemaweg 80, 3015 CN, Rotterdam, the Netherlands
Correspondence: Julia Goncharenko (i.goncharenko@herts.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P255
Epilepsy is one of the most prevalent neurological diseases in humans,
affecting people of all ages. One of the most common forms of epilepsy in children is absence epilepsy [1]. Characteristic symptoms of
absence epilepsy are sudden seizures that are accompanied by periods of behavioral arrest and impaired consciousness [1]. As in other
forms of epilepsy, these seizures are electrophysiologically described
by neuronal oscillations in thalamo-cortical networks and appear as
generalized spike-and wave discharges (GSWDs) in the electroencephalogram (EEG) [2]. Oscillatory activity in cerebral cortex and thalamus
can be caused by excessive inhibition in thalamus or by excessive cortical activity [2]. It has been suggested that the initiation of absence
seizures can be triggered by events that switch neuronal activity in
thalamo-cortical networks from normal asynchronous activity to synchronised oscillations [2].
Previous experimental studies have shown that oscillatory activity in
thalamo-cortical networks and the accompanying GSWDs can be disrupted by stimulation of the thalamus [3]. Recently, it has been found
that optogenetic activation of neurons in the cerebellar nuclei (CN)
is a powerful tool to stop epileptic absence seizures using a closedloop system in two unrelated mouse models [4]. Due to their anatomical bottleneck location, CN neurons can control the balance of
excitation and inhibition in thalamus, resetting the oscillatory activity
in thalamo-cortical loops. However, the mechanism underlying the
disruption of thalamo-cortical oscillations and absence seizures by
stimulation of the CN remains unknown.
Here we use computer simulations to investigate the mechanisms
underlying the termination of absence seizures by optogenetic stimulation of CN neurons. We simulate a thalamo-cortical network model
of adaptive exponential integrate-and-fire neurons, displaying complex intrinsic properties such as low-threshold spiking, regular spiking, fast spiking and adaptation [5]. The network activity can exhibit
oscillatory or asynchronous irregular (AI) dynamics, depending on
the level of adaptation in cortical cells [5]. We use electrophysiologically recorded spike trains that result from optogenetic activation
of CN neurons in mouse models of absence epilepsy as input to the
network model to analyse the mechanism of reverting abnormal
oscillatory activity to the normal AI state. Our results illustrate how
input from the CN can control oscillatory activity in thalamo-cortical
networks and therefore provide a mechanism to terminate epileptic
absence seizures.
BMC Neurosci 2017, 18(Suppl 1):60
References
1. Berg AT, Berkovic SF, Brodie MJ, et al.: Revised terminology and concepts for
organization of seizures and epilepsies: report of the ILAE Commission on
Classification and Terminology, 2005–2009. Epilepsia 2010, 51:676–685.
2. Snead OC III: Basic mechanisms of generalized absence seizures. Ann
Neurol 1995, 37(2):146–157.
3. Paz JT, Davidson TJ, Frechette ES, et al.: Closed-loop optogenetic control of
thalamus as a tool for interrupting seizures after cortical injury. Nat Neurosci
2013, 16:64–70.
4. Kros L, Eelkman Roda OHJ, Spanke JK, et al.: Cerebellar Output Controls
Generalised Spike-and-Wave Discharge Occurrence. Ann Neurol 2015,
77(6):1027–1049.
5. Destexhe A.: Self-sustained asynchronous irregular states and up-down
states in thalamic, cortical and thalamocortical networks of nonlinear
integrate-and-fire neurons. J Comput Neurosci 2009, 27:493–506.
P256
The effect of homeostatic structural plasticity on associative
memory in a network with spike‑time dependent inhibitory
synaptic plasticity
Ankur Sinha, Christoph Metzner, Roderick Adams, Michael Schmuker, Neil
Davey, Volker Steuber
UH Biocomputation Group, University of Hertfordshire, Hatfield, AL10
9AB, UK
Correspondence: Ankur Sinha (a.sinha2@herts.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P256
The stability of neuronal networks that are continuously modified by
various activity dependent processes and their robustness to lesions
or deafferentation necessitate the co-existence of complementary
homeostatic mechanisms [1]. Recent research has studied these
homeostatic plasticity mechanisms using both experiments and computational modelling.
In a computational study of homeostatic synaptic plasticity, Vogels
and collaborators showed that inhibitory synaptic plasticity governed
by a symmetric spike timing dependent plasticity rule successfully stabilises a spiking neuronal network to an asynchronous irregular (AI)
state, as is observed in the cortex [2]. The proposed model also permitted the storage and recall of non-attractor Hebbian associative memories in the network.
Butz and van Ooyen recently presented a spiking neural network
model of homeostatic structural plasticity [3]. In their study, neurons
in the network attempt to maintain a fixed level of electrical activity by
forming or breaking synaptic connections as required. The structural
reorganisation of the network is also shown to replicate experimentally observed aspects of the restructuring of the visual cortex following deafferentation by focal retinal lesions [4, 5].
In the present study, we investigate the capacity of a cortical network
model balanced by homeostatic inhibitory plasticity to store and recall
non-attractor Hebbian associative memories. Extending our previous
work [6], we investigate the functional effect of homeostatic structural
plasticity on associative memory performance during network deafferentation and repair. We explore the interaction between the two
homeostatic mechanisms, inhibitory spike-time dependent synaptic
plasticity and structural plasticity, and investigate how the experimentally observed AI state is affected by the coexistence of these two
homeostatic mechanisms that operate on different time scales. Furthermore, we discuss enhancements to the model of structural plasticity aimed at increasing biological plausibility and study their effect
on memory capacity. Finally, we report on the variation in associative
memory performance during network deafferentation and repair, and
discuss the parameters that affect it.
References
1. Turrigiano GG: Homeostatic plasticity in neuronal networks: the more
things change, the more they stay the same. Trends in neurosciences 1999,
22(5): 221–227.
2. Vogels T, Sprekeler H, Zenke F, Clopath C, Gerstner W: Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory
networks. Science 2011, 334:1569–1573.
Page 146 of 176
3. Butz M, van Ooyen A: A simple rule for dendritic Spine and axonal bouton
formation can account for cortical reorganization after focal retinal lesions.
PLoS Comput Biol. 2013, 9(10): e1003259
4. Keck T, Mrsic-Flogel TD, Afonso MV, Eysel UT, Bonhoeffer T, Hübener M:
Massive restructuring of neuronal circuits during functional reorganization
of adult visual cortex. Nature Neuroscience 2008, 11(10): 1162–1167.
5. Yamahachi H, Marik SA, McManus JN, Denk W, Gilbert CD: Rapid axonal
sprouting and pruning accompany functional reorganization in primary
visual cortex. Neuron 2009, 64(5): 719–729.
6. Sinha A, Davey N, Adams R, Steuber V: Structural plasticity and associative
memory in balanced neural networks with spike-time dependent inhibitory plasticity. BMC Neuroscience 2015, 16(1): P235.
P257
The dependence of arithmetic operations on input location
in cerebellar nucleus and cortical pyramidal neurons
Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Nielsen,
Michael Schmuker, Volker Steuber
Centre for Computer Science and Informatics Research, University
of Hertfordshire, Hatfield, AL10 9AB, UK
Correspondence: Maria Psarrou (m.psarrou@herts.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P257
Neurons are constantly bombarded with numerous synaptic signals,
which are integrated in order to generate output spikes. A simple way
to depict neuronal computations is to plot the relationship between the
neuronal input rate and the corresponding output spike rate, that is, the
Input - Output relationship (I-O, or transfer function) [1]. A change in the
slope or gain of the I-O curve in the presence of different cellular and
synaptic mechanisms, such as synaptic noise, shunting inhibition or synaptic plasticity is an indicator of ongoing multiplicative operations [1–4].
Gain modulation is a brain-wide principle of neuronal computation,
enabling nonlinear combinations of sensory and cognitive information. An essential component of gain modulation is that a modulatory
input alters the sensitivity of the neuron to the original (driving) input,
without changing its selectivity [5]. Different nonlinearities in the relationships between input firing rate, excitatory synaptic conductance
and output firing rate have been shown to underlie gain modulation [2,
4]. In the present study, we investigate in two different types of neurons
whether the dendritic location of excitatory input affects the arithmetic
operation performed by different modulatory inhibitory inputs. We used
two well described morphologically realistic conductance based models, a cerebellar nucleus (CN) neuron model [6] and a layer V pyramidal
neuron model [7], and we explore various driving and modulatory input
conditions. Modulatory input was provided either by distributed synaptic inhibitory input or a tonic somatic inhibitory conductance. When
the driving and modulatory input were both of synaptic nature, we
observed a correlation between the distance of the excitatory driving
input from the soma and the extent of the multiplicative gain change
in both the CN and the layer V pyramidal neurons. In the CN neuron,
we found that excitatory inputs underwent additive operations when
delivered in somatic and perisomatic areas, and multiplications when
delivered to distal dendritic areas. In contrast, in the layer V pyramidal
neuron excitatory driving input was always multiplied, independent of
the synapse location. In all cases where inputs underwent multiplicative operations, the mapping between synaptic excitatory conductance
and output firing rate revealed a nonlinearity, with more pronounced
nonlinearities due to dendritic saturation in distal synaptic locations
corresponding to larger multiplicative gain changes. To show that these
non-linear mappings between input conductance and output rate were
the basis of the multiplicative gain changes, we drove the two neuronal types with excitatory current injections, at the soma or different
dendritic locations, in the presence of modulatory tonic somatic inhibition. In this case, the arithmetic operations performed in all distinct
neuronal locations were additive shifts. Moreover, synaptic inhibition
had a greater effect on neuronal output than somatic tonic inhibition.
Our results indicate that the location and the nature of excitatory inputs
affect in a systematic way whether the input undergoes a multiplicative
or additive operation. The extent of these operations is also related to
the nature of the inhibitory input. Furthermore, different neuronal types
BMC Neurosci 2017, 18(Suppl 1):60
might perform different operations when the inputs are received in
their perisomatic areas.
References
1. Silver RA. Neuronal arithmetic. Nat Rev Neurosci. 2010; 11:474–89.
2. Prescott S a, De Koninck Y. Gain control of firing rate by shunting inhibition:
roles of synaptic noise and dendritic saturation. Proc. Natl. Acad. Sci. U. S. A.
2003; 100:2076–81.
3. Chance FS, Abbott LF, Reyes AD. Gain modulation from background synaptic input. Neuron 2002; 35:773–82.
4. Rothman JS, Cathala L, Steuber V, Silver RA. Synaptic depression enables
neuronal gain control. Nature 2009; 457:1015–8.
5. Salinas E, Sejnowski TJ. Gain modulation in the central nervous system:
where behavior, neurophysiology, and computation meet. Neuroscientist
2001; 7:430–40.
6. Steuber V, Schultheiss NW, Silver RA, De Schutter E, Jaeger D. Determinants
of synaptic integration and heterogeneity in rebound firing explored with
data-driven models of deep cerebellar nucleus cells. J. Comput. Neurosci.
2011; 30:633–58.
7. Hay E, Hill S, Schürmann F, Markram H, Segev I. Models of neocortical layer
5b pyramidal cells capturing a wide range of dendritic and perisomatic
active properties. PLoS Comput. Biol. 2011; 7
P258
A Framework for Automated Validation and Comparison
of Models of Neurophysiological and Neurocognitive Biomarkers
of Psychiatric Disorders
Christoph Metzner1, Achim Schweikard2, Tuomo Mäki-Marttunen3,
Bartosz Zurowski4 and Volker Steuber1
1
Centre for Computer Science and Informatics Research, University
of Hertfordshire, College Lane, Hatfield, AL10 9AB, United Kingdom;
2
Institute for Robotics and Cognitive Systems, University of Luebeck,
Luebeck, 23562, Germany; 3NORMENT, Institute of Clinical Medicine,
University of Oslo, Oslo, Norway; 4Department of Psychiatry, University
of Luebeck, Schleswig-Holstein, Luebeck, 23562, Germany
Correspondence: Christoph Metzner (c.metzner@herts.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P258
Research on psychiatric disorders has gradually shifted its focus from
complex clinical phenotypes towards the identification of biomarkers and endophenotypic measures. Computational approaches have
gained significantly more attention over the last years, and this has
led to the emergence of ‘Computational Psychiatry’ as an independent
discipline. Computational modelling of biomarkers promises to more
readily shed light on the mechanisms underlying disorders and to
facilitate the discovery of novel medications [1]. However, in order to
develop a computational model, scientists need to have an in-depth
understanding of the current, relevant experimental data, the current
state of computational modeling and the state-of-the-art of statistical
testing. Based on this knowledge, they have to choose the appropriate criteria with which the model predictions and experimental observations will be compared [2]. In a field where both the number of
experimental and computational studies grows rapidly, as is the case
for psychiatry, this becomes more and more impracticable. Omar et al.
therefore proposed a framework for automated validation of scientific
models, SciUnit [3]. Here, we propose to adopt this framework for the
computational psychiatry community and to collaboratively build
common repositories of experimental observations, computational
models, test suites and tools. As a case in point, we have implemented
test suites for auditory steady-state response deficits in schizophrenic
patients, which are based on observations from several experimental
studies [4–6], and we demonstrate how existing computational models [6, 7] can be validated against these observations and compared
against each other. We have included sets of observations from three
experimental studies, which concur on most findings but also disagree
on some. This allows us to demonstrate the usefulness of our approach
in highlighting and clarifying existing, potentially conflicting, experimental data. We have included computational models that not only
comprise biophysically detailed as well as abstract models, but that
also differ in implementation (native Python vs. Genesis vs NeuroML2),
in order to demonstrate the flexibility of the approach. Furthermore,
Page 147 of 176
this additionally allows us to showcase the ability of the framework to
compare models against each other based on a set of experimental
observations. Furthermore, our approach enables us to assess the variability of the produced model output, and therefore the robustness of
the findings, by generating a distribution of model instances where
certain parameters, such as the precise timing of noise (however, not
strength and type of noise) or the precise connectivity (however, not
the distribution of connections) vary, which then are used to produce
a distribution of model outputs. This can inform on the robustness of
the findings and be compared against the variability of experimental
observations.
References
1. Siekmeier, P.: Computational modeling of psychiatric illnesses via welldefined neurophysiological and neurocognitive biomarkers. Neurosci
Biobehav Rev 2015, 57: 365–380
2. Gerkin, R.C. and Omar, C.: NeuroUnit: Validation Tests for Neuroscience
Models. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013
3. Omar, C., Aldrich, J., and Gerkin, R.C.: Collaborative infrastructure for testdriven scientific model validation. In CompanionProceedings of the 36th
International Conference on Software Engineering, ACM, 2014.
4. Kwon J.S., O’Donnell B.F., Wallenstein G.V., Greene R.W., Hirayasu Y., Nestor
P.G., Hasselmo M.E., Potts G.F., Shenton M.E., and McCarley R.W..: Gamma
frequency–range abnormalities to auditory stimulation in schizophrenia.
JAMA Psychiatry 1999, 56(11):1001–1005
5. Krishnan, G.P., Hetrick, W.P., Brenner, C.A., Shekhar, A., Steffen, A.N., and
O’Donnell, B.F.: Steady state and induced auditory gamma deficits in
schizophrenia. Neuroimage 2009 47(4):1711–1719
6. Vierling-Claassen, D., Siekmeier, P., Stufflebeam, S., and Kopell, N.: Modeling
GABA alterations in schizophrenia: a link between impaired inhibition and
altered gamma and beta range auditory entrainment.
J Neurophysiol 2008, 99(5):2656–2671
7. Metzner, C., Schweikard, A. and Zurowski, B.: Multi-factorial modeling of
impairment of evoked gamma range oscillations in schizophrenia. Front
Comp Neurosci 2016, 10
P259
Synergetic and redundant information flow in dynamical systems:
an operative definition based on prediction
Daniele Marinazzo1, Luca Faes2, Sebastiano Stramaglia3
1
Department of Data Analysis, Ghent University, Ghent, B9000,
Belgium; 2BIOtech, Dept. of Industrial Engineering, University of Trento,
and IRCS-PAT FBK, 38010 Trento, Italy; 3Dipartimento di Fisica, Università
degli Studi Aldo Moro, Bari, and INFN, Sezione di Bari, 70123 Bari, Italy
Correspondence: Daniele Marinazzo (daniele.marinazzo@ugent.be)
BMC Neuroscience 2017, 18 (Suppl 1):P259
Information theoretic treatment of groups of correlated degrees of
freedom can reveal their functional roles as memory structures or
information processing units. Furthermore, by looking at the common amount of information shared in a group of variables we can
tell whether they are mutually redundant or synergetic. The application of these insights to identify functional connectivity structure is
a promising line of research. Another topic of general interest is the
understanding of couplings between dynamical systems and their
parts. Transfer entropy and Granger causality are popular approaches
used to distinguish effectively driving and responding elements and
to detect asymmetry in the interaction of subsystems. These two
methods can be unified under some conditions, opening new computational and methodological perspectives. Several techniques can evidence sets of variables which provide information for the future state
of the target. This information can be synergetic or redundant, with
important implication on our understanding of the functioning of the
dynamical system under analysis.
Importantly, not taking into account the joint dynamical influence of
two or more variables can lead to bias and wrong estimations of links
(false positive and false negatives).
In the field of information theory these concepts are often defined
and studied by means of axioms. Here we will instead use an operative
definition based on reduction in variance, using the unnormalized version of Granger causality. We will present an application to simulated
BMC Neurosci 2017, 18(Suppl 1):60
Page 148 of 176
datasets and neuroimaging data, such as the one depicted in Figure 1,
where average redundant and synergetic contributions, computed on
116 brain regions from 90 subjects from the Human Connectome Project dataset are depicted.
Figure 1. Synergetic and redundant influences between 116 brain
regions from the AAL template, averaged over 90 subjects from
the HCP dataset. A: matrix of synergetic/redundant contributions
(top) and dendrograms (bottom). B: Redundant and synergetic
contributions for two representative regions, a cortical one (top)
and a cerebellar one (bottom)
Reference
1. Stramaglia S, Angelini L, Wu G, Cortes J, Faes L, Marinazzo D: Synergetic
and redundant information flow detected by unnormalized Granger
causality: application to resting state fMRI. IEEE Trans. Biomed. Eng. 2016, 63
(12):2518–2524.
P260
Forming and Using Hierarchical Cognitive Maps: a Neural Network
Model
Henry O. C. Jordan, Simon M. Stringer
OFTNAI, Dept. Experimental Psychology, University of Oxford, South Parks
Road, OX1 3UD, Oxford, UK
Correspondence: Henry O. C. Jordan (hocjordan@live.co.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P260
Clear evidence exists that model-based planning using cognitive maps
occurs in mice and humans [1] and such planning has been modelled
by researchers such as Gaussier et al.[2] and Ponulak & Hopfield [3]
using a gradient or propagating-wave approach. It is also well-known
that planning can be hierarchical [4]; such hierarchical planning has
been explored by computer scientists [5, 6] but almost exclusively in
model-free reinforcement learning tasks. We combine these strands of
research to create an unsupervised neural network model of hierarchical
planning using cognitive maps [1, 3] and policy-based option discovery [6]. Our model:
•
•
•
•
•
Receives sensory information from a simulated agent as it
explores a grid environment.
Stores the structure of that environment within recurrent neural
connections as a cognitive map.
Uses this internal map to reach goal states via a propagating wave
front mechanism.
Solves a set of planning tasks and identifies common elements
(‘options’) within those solutions.
Uses these common elements (‘options’) to speed up the planning
process for further tasks.
Our model plans by causing a wave of neural activation to propagate
from the goal state to the agent’s current state. Learned options act
as shortcuts for this wave, allowing the agent to speed up navigation
through well-travelled areas (Fig. 1). We also demonstrate that options
provide more benefit in larger, more structured environments.
Conclusion: Previous work [5, 6] has shown that including options
increases an agent’s ability to accumulate reward. In contrast, we show
that learning and using options can instead increase the processingper-step efficiency of action selection when making decisions in a
learned environment. Our model also demonstrates that such hierarchical learning and planning can be performed by an unsupervised
neural network and therefore hints at a biological implementation.
Figure 1. Planning per Action, with (+) and without (-) learned
options. This box plot shows how many time steps an agent
requires to select its next action, averaged over 100 trials in each of
four different environments: an open environment without obstacles, a structured maze with four rooms, a big open environment
with 4x as many states, and a big maze
Acknowledgements
We thank the Oxford Foundation for Theoretical Neuroscience and AI
(OFTNAI) for supporting this work.
References
1. Dolan RJ, Dayan P. Goals and habits in the brain. Neuron. 2013. p. 312–25.
2. Gaussier P, Revel A, Banquet JP, Babeau V. From view cells and place cells to
cognitive map learning: Processing stages of the hippocampal system. Biol.
Cybern. 2002;86:15–28.
3. Ponulak F, Hopfield JJ. Rapid, parallel path planning by propagating wavefronts of spiking neural activity. Front. Comput. Neurosci. 2013;7:98.
4. Botvinick MM, Niv Y, Barto AC. Hierarchically organized behavior and its
neural foundations: a reinforcement learning perspective. Cognition.
2009;113:262–80.
5. Sutton RS, Precup D, Singh S. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artif. Intell.
1999;112:181–211.
6. Girgin S, Polat F, Alhajj R. Improving reinforcement learning by using
sequence trees. Mach. Learn. 2010;81:283–331.
P261
Harmonic SSEP Spectra are Determined by Modulation
of Population Firing Rate ‑ a Modeling Study
Elżbieta Gajewska-Dendek, Piotr Suffczyński
Department of Biomedical Physics, Institute of Experimental Physics,
University of Warsaw, Warsaw, 02-093 Poland
Correspondence: Elżbieta Gajewska-Dendek (egd@fuw.edu.pl)
BMC Neuroscience 2017, 18 (Suppl 1):P261
Steady State Evoked Potentials (SSEP) are EEG signal responses to
periodically changing stimulus. SSEPs consist of a strong fundamental
response and sometimes also its harmonic and subharmonic frequencies. The SSEP can be observed in visual, auditory and somatosensory
modalities. Despite multiple applications of SSEP in cognitive neuroscience, clinical neuroscience and brain computer interfaces (BCI),
some basic questions concerning this phenomenon still remain open:
what is the physiological mechanism of generation of harmonic spectra and what determines relative spectral power of SSEP at fundamental frequency and at the harmonics.
The aim of this study was to investigate the SSEP generation mechanisms and its characteristic with a realistic computational model. The
presented results are an extension from previously published version
[1]. The model consists of single compartment excitatory and inhibitory cells of the Hodgkin-Huxley type, arranged in multiple cortical
columns. The network contains 8000 neurons and more than 106 synapses, based on connectivity data from cat primary visual cortex. The
sensory stimulus is modeled as 7 to 50 Hz square or sine modulated
rate of Poisson process. The simulated EEG signal is as a sum of synaptic currents of all pyramidal neurons. Additionally, for the square
stimulus, we varied duty cycle: 50% (default), 33% and 66% in order
to investigate whether SSEP spectral power depends on magnitude of
BMC Neurosci 2017, 18(Suppl 1):60
ON and OFF responses or on the overall energy of the stimulus. The
magnitude of transient responses was determined by firing adaptation strength of excitatory cells by modulating the conductivity of Cadependent potassium current. We compare the simulation data with
experimental EEG recordings obtained in somatosensory cortex during vibrotactile stimulation as well as from visual cortex in response to
flickering stimuli. The spectra of modeled SSEP exhibit fundamental
and higher harmonic frequencies, similarly to experimental observations. The neurons firing rates are approximately constant and much
lower than stimulus frequencies. The network oscillation emerges
from irregular and sparse firing of individual neurons but in phase
with the population fundamental rhythm. The harmonic frequencies
cannot be directly related to firing of individual neurons but rather to
EEG waveform resulting from overall network activity. Additionally, our
modeling study shows that the SSEP power is dependent on both the
stimulus duty cycle and degree of adaptation: in general, the largest
spectral power of dominant frequency was observed for duty cycle
50%, and medium adaptation strength. The signal energy increased
for lower duty cycle (<66%) and low adaptation, or for higher duty
cycle (>33%) and stronger neuronal adaptation.
References
1. Gajewska-Dendek E, Suffczyński P: Investigation of SSEP by means of a realistic computational model of the sensory cortex. In: Villa AEP, Masulli P, Pons
RAJ. Artificial Neural Networks and Machine Learning - ICANN 2016: 25th
International Conference on Artificial Neural Networks, Barcelona, Spain,
September 6-9, 2016, Proceedings, Part I. Springer International Publishing;
2016. p. 532.
P262
Computational measure to account for erroneous neural
deactivation when oxygen supply cannot meet metabolic demand
in neuroimaging studies
Nicoladie Tam1, George Zouridakis2, Luca Pollonini2
1
Department of Biological Sciences, University of North Texas, Denton,
TX 76203, USA; 2Departments of Engineering Technology, Computer
Science, and Electrical and Computer Engineering, University of Houston,
Houston, TX, 77204, USA
Correspondence: Nicoladie Tam (nicoladie.tam@unt.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P262
Introduction: Functional near-infrared spectroscopy (fNIRS) is an
emerging optical imaging technique that can detect the neural activation/deactivation based on the optical absorption characteristics of
both oxy-hemoglobin (oxy-Hb) and deoxy-hemoglobin (deoxy-Hb).
The supply of oxygen to neural tissues can be affected not only by
vasoconstriction or vasodilation, but also by the limited capacity of
the system to supply more oxygen when demand exceeds the maximum oxygen availability. When this rate-limiting condition occurs,
a decrease in oxygen availability is detected that can be erroneously
interpreted as deactivation. Thus, absolute changes in oxy-/deoxyHb concentrations do not always represent neural activation/deactivation. It is therefore necessary to develop alternative measures to
account for such a paradox, so that a decrease in Hb concentration is
not misinterpreted as deactivation.
Methods: Rather than using absolute oxy-Hb and deoxy-Hb concentration as the metric, we propose to normalize these measures by the
total blood volume (oxy-Hb + deoxy-Hb), so that they become oxyHb/(oxy-Hb + deoxy-Hb) and deoxy-Hb/(oxy-Hb + deoxy-Hb), respectively. We observed in previous studies [1–4] that the oxygen demand
could exceed the oxygen supply in the cortex for such a motor task.
To test the proposed measures of hemodynamic responses, 75 human
subjects were recruited to perform arm movements in two orthogonal
directions (front-back and left-right) while we recorded the hemodynamic responses from the motor and prefrontal cortices.
Results: Using the proposed measures, we were able to detect the
relative changes in oxy- and deoxy-Hb concentrations in relation to
the blood supply (i.e., the total blood volume). In most circumstances,
when deoxy-Hb (oxygen extraction) concentration increased, oxy-Hb
(oxygen delivery) concentration decreased simultaneously. In certain phases of movement execution, oxygen extraction (deoxy-Hb)
Page 149 of 176
appeared to remain constant after it had increased, while oxygen
delivery (oxy-Hb) continued to decrease. When oxygen availability was
restricted, the ability to extract more oxygen was also limited, resulting in an apparent maxed-out response. The analysis showed that
the paradoxical hemodynamic changes in deoxy-Hb could be compensated by the normalized measures. This metric could indicate an
increase in normalized deoxy-Hb response (oxygen demand), in spite
of a detected decrease in both deoxy-Hb (oxygen extraction) and oxyHb (oxygen delivery) concentrations.
Conclusions: The proposed normalized oxy- and deoxy-Hb measures
can correctly detect relative changes in oxygen demand with respect
to the available oxygen (oxy-Hb + deoxy-Hb), even when oxygen supply cannot meet demand. We have established an alternative measure
to account for the erroneous interpretation of neural deactivation.
References
1. Tam ND, Pollonini L, Zouridakis G: Decoding movement direction using
phase-space analysis of hemodynamic responses to arm movements
based on functional near-infrared spectroscopy. In: 38th Annual International Conference of the IEEE Engineering in Medicine & Biology Society: August
16-20, 2016 2016; Orlando, FL: IEEE; 2016: 1580–1583.
2. Tam ND, Pollonini L, Zouridakis G: Phase space analysis of hemodynamic
responses to intentional movement directions using functional nearinfrared spectroscopy (fNIRS) optical imaging technique. In: 25th Annual
Computational Neuroscience Meeting: CNS-2016: July 2–7, 2016 2016; Jeju,
South Korea; 2016: 54.
3. Tam ND, Zouridakis G: Temporal decoupling of oxy- and deoxy-hemoglobin hemodynamic responses detected by functional near-infrared
spectroscopy (fNIRS). Journal of Biomedical Engineering and Medical Imaging
2014, 1(2):18–28.
4. Tam ND, Zouridakis G: Decoding movement direction from motor cortex
recordings using near-infrared spectroscopy. In: Infrared Spectroscopy:
Theory, Developments and Applications. edn. Hauppauge, NY: Nova Science
Publishers, Inc.; 2014.
P263
Detecting brain hubs following brief mindfulness training
Yi-Yuan Tang1, Rongxiang Tang2
1
Department of Psychological Sciences, Texas Tech University, Lubbock,
TX 79409, USA; 2Department of Psychological & Brain Sciences,
Washington University in St. Louis, St. Louis, MO 63130, USA
Correspondence: Yi-Yuan Tang (yiyuan.tang@ttu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P263
Many studies have shown that mindfulness training improves attention control, emotion regulation and cognitive performance through
changing brain activity and the efficiency of brain networks [1–4].
Graph theory analysis could reveal the role of specific functional areas
that are particularly important for integrating information across the
whole-brain networks, called hubs [5, 6]. However, what hubs involve
in mindfulness training and support positive changes are not well
understood. Here, we applied a novel graph theory analysis to restingstate fMRI data to identify brain hubs induced by a brief mindfulness
training - integrative body–mind training (IBMT), which was previously
reported in our series of randomized studies [1–4].
Forty-two (21 ± 1.6 years old) healthy college students were recruited
and randomly assigned to an IBMT group or a relaxation group (RT).
The participants had no previous training experience and received
4 weeks of IBMT or RT training with 30 min per session for 20 sessions (~10 h training in total). All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was
approved by the local Institutional Review committee. Neuroimaging
data was collected using a 3-Telsa Siemens Allegra scanner and preprocessed following the standard procedures included slice timing,
motion correction, regression of WM/CSF signals and spatial normalization [4]. For network parcellation and construction, we used a wellvalidated parcellation scheme consisting of 333 cortical parcels that
are distributed across the brain and assigned to 13 different functional
networks [5]. We applied network-based approach towards neuroimaging data and two network measures - global efficiency and participation coefficient were computed based on literature [6]. Compared
BMC Neurosci 2017, 18(Suppl 1):60
References
1. Tang YY, Holzel BK, Posner MI: The neuroscience of mindfulness meditation.
Nat Rev Neurosci. 2015, 16: 213–225.
2. Tang YY, et al.: Short-term meditation training improves attention and
self-regulation. Proceedings of the National Academy of Sciences, USA. 2007,
104:17152–17156.
3. Tang YY, et al.: Central and autonomic nervous system interaction is altered
by short term meditation. Proceedings of the National Academy of Sciences,
USA. 2009, 106: 8865–70
4. Tang YY, Tang R, Posner MI: Brief meditation training induces smoking
reduction. Proceedings of the National Academy of Sciences, USA. 2013, 110:
13971–13975.
5. Gordon EM, et al.: Generation and evaluation of a cortical area parcellation
from resting-state correlations. Cerebral Cortex 2016, 26: 288–303.
6. Rubinov M, Sporns O: Complex network measures of brain connectivity:
uses and interpretations. Neuroimage 2010, 52: 1059–1069.
P264
Interplay between propagation delay and frequency of oscillation
determines emergent structures of neuronal networks driven
by triplet‑based STDP
Mojtaba Madadi Asl1, Alireza Valizadeh1,2, Peter A. Tass3
1
Department of Physics, Institute for Advanced Studies in Basic Sciences
(IASBS), Zanjan, 45195-1159, Iran; 2School of Cognitive Sciences, Institute
for Research in Fundamental Sciences (IPM), Tehran, 19395-5746, Iran;
3
Department of Neurosurgery, School of Medicine, Stanford University,
Stanford, CA, 94305, USA
Correspondence: Mojtaba Madadi Asl (m.madadi@iasbs.ac.ir)
BMC Neuroscience 2017, 18 (Suppl 1):P264
Spike-timing-dependent plasticity (STDP) adjusts synaptic strengths
according to the relative timing of pre- and postsynaptic spikes [1].
The classic STDP rule eliminates bidirectional connections between
two coupled neurons and turns them into unidirectional connections
[2, 3]. As shown recently, by taking into account dendritic and axonal
propagation delays, the conventional pair-based additive STDP may
lead to both unidirectional and bidirectional connections, or decouple neurons by weakening reciprocal connections in both directions
[4]. The triplet-based STDP, however, employs triplets of spikes that
modify synaptic strengths [5]. Hence, the latter captures the effect of
frequency of oscillations on the pre-post pairing. Here, we provide
a general theoretical framework by assuming that the neurons are
phase-locked with a phase lag which is determined by the temporary
values of the synaptic strengths, propagation delays, frequency of
oscillation, and the phase sensitivity of the neurons, and explore how
the final configuration of the system can be predicted. In the absence
of propagation delays, low-frequency oscillation leads to unidirectional connection for both pair- and triplet-based STDP. However, for
higher frequencies, the triplet-based model has a tendency to achieve
bidirectional connections, but results for the pair-based are the same
as in the low-frequency regime. We show that employing triplet-based
STDP leads to diverse connectivity patterns of oscillatory neurons in
the presence of propagation delays, which qualitatively differ from the
results obtained by pair-based STDP. In particular, large axonal propagation delay in the high-frequency regime is associated with a stable
decoupling of both reciprocal synapses when the neurons are in a
phase-locked state (see Figure 1F).
C
B
g21
2.0
0.5
(ms)
3.0
1.0
1.0
0.0
0.0
D
E
F
3.0
1.0
2.0
0.5
(ms)
Acknowledgements
This work was supported by the Office of Naval Research.
A
g21
to RT, after 10 h training, IBMT induced significant reduction of global
efficiency in the midline default mode network (DMN) and increased
participation coefficient at ventral anterior cingulate cortex (vACC).
Conclusions: This study utilized a novel graph theory analysis of
functional networks to assess the brain efficiency and participation
of hubs following brief mental training. Consistent with our and other
research, our results suggest that brief mindfulness training IBMT significantly reorganizes DMN activity and network efficiency that may
reallocate more resources for better self-control through the key hub
in the vACC.
Page 150 of 176
1.0
0.0
0.0
0.5
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1.0
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0.0
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1.0
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g12
1.0
Figure 1. Theoretical prediction of triplet-based synaptic plasticity modification. A-C. Low-frequency regime. The colors show the
phase lag of spiking of the neurons derived from the joint phase
model and the vector field shows the direction of change in the
joint synaptic strengths. The yellow curves denote the simulated
synaptic strengths for a random initial value. D-F. High-frequency
regime. Total time of simulations is 10 s. The dendritic delay is fixed
at 0.2
References
1. Bi GQ, Poo MM: Synaptic modifications in cultured hippocampal neurons:
dependence on spike timing, synaptic strength, and postsynaptic cell type.
J Neurosci 1998, 18(24): 10464–10472.
2. Song S, Miller KD, Abbott LF: Competitive Hebbian learning through spiketiming-dependent synaptic plasticity. Nat Neurosci 2000, 3(9): 919–926.
3. Bayati M, Valizadeh A: Effect of synaptic plasticity on the structure and
dynamics of disordered networks of coupled neurons. Phys Rev E 2012,
86(1): 011925.
4. Madadi Asl M, Valizadeh A, Tass PA: Dendritic and Axonal Propagation
Delays Determine Emergent Structures of Neuronal Networks with Plastic
Synapses. Sci Rep 2017, 7(39682). doi: 10.1038/srep39682.
5. Pfister JP, Gerstner W: Triplets of spikes in a model of spike timing-dependent plasticity. J Neurosci 2006, 26(38): 9673–9682.
P265
Plasticity and network implications of a synaptic LPA‑signalling
pathway
Andreas Nold1†, Wei Fan2, Sara Konrad1, Heiko Endle2, Johannes Vogt2†,
Tatjana Tchumatchenko1†
1
Theory of Neural Dynamics, Max Planck Institute for Brain Research,
60438 Frankfurt, Germany; 2Institute for Microscopic Anatomy
and Neurobiology, University Medical Center, Johannes Gutenberg
University, 55131 Mainz, Germany
Correspondence: Andreas Nold (andreas.nold@brain.mpg.de)
†
equal contribution
BMC Neuroscience 2017, 18 (Suppl 1):P265
We explore the implications of a synaptic signalling pathway involving the plasticity-related gene 1 (PRG-1), a molecule which is located
at the postsynaptic membrane and modulates glutamatergic transmission, for the stability of the steady states of cortical circuit models.
An accurate synaptic function is of crucial importance for learning
and memory formation. Recently, the importance of this postsynaptic control element has been shown for synaptic signalling: Deletion
of PRG-1 in mice leads to neuronal hyper excitability [1], and a single
nucleotide polymorphism (SNP) in the PRG-1 gene affecting approx. 5
million European and US citizens is linked to psychiatric disorders such
as schizophrenia [2].
PRG-1 modulates glutamatergic transmission via its ability to take
up lysophosphatidic acid (LPA), which is synthetized by autotaxin,
from the synaptic cleft [1, 2]. Inhibition of LPA-uptake, as present in
PRG-1 deficient mice, leads to elevated synaptic LPA levels which
BMC Neurosci 2017, 18(Suppl 1):60
via LPA2-receptors lead to higher levels of presynaptic intracellular
Ca2+, higher vesicle release probabilities, and ultimately to neuronal
hyperexcitability. This pathway was recently suggested to modulate
synaptic short-term plasticity properties in the hippocampus [3]. Presynaptically, the pathway is modulated by glutamate which stimulates
autotaxin (ATX) activity and thereby LPA-synthesis.
Here, we present a synaptic model implementing this LPA-based signalling pathway as an extension of the classical Tsodyks-Markram
model for short-term synaptic plasticity [4], and explore the implications at a single synapse level, depending on increasing pre- and
postsynaptic firing rates. The former leads to higher levels of LPA production via ATX, therefore increasing the LPA-concentration in the
synaptic cleft, and elevating the presynaptic Calcium concentration,
which leads to higher vesicle release probabilities. The latter modulates the activity of LPA-uptake via PRG-1. Implications are explored for
short-term facilitation and depression of synapses, both for wild-type,
as well as PRG-1 deficient cases.
We propose an efficient network implementation and analyze how
loss of PRG-1 function affects the steady states of cortical circuits models, characterized by large, sparsely connected spiking networks in an
asynchronous balanced excitation-inhibition regime. Results for bistable states of the network [5] are presented.
Acknowledgements
We acknowledge financial support from DFG through the CRC1080
and from Max Planck Society.
References
1. Trimbuch T, Beed P, Vogt J, Schuchmann S, Maier N, Kintscher M, Breustedt
J, Schuelke M, Streu N, Kieselmann O, Brunk I. Synaptic PRG-1 modulates
excitatory transmission via lipid phosphate-mediated signaling. Cell 2009,
138(6):1222–35.
2. Vogt J, Yang JW, Mobascher A, Cheng J, Li Y, Liu X, Baumgart J, Thalman C,
Kirischuk S, Unichenko P, Horta G. Molecular cause and functional impact
of altered synaptic lipid signaling due to a PRG‐1 gene SNP. EMBO Mol Med
2016, 8(1):25–38.
3. Vogt J, Kirischuk S, Unichenko P, Schlüter L, Pelosi A, Endle H, Yang JW, Schmarowski N, Cheng J, Thalman C, Strauss U. Synaptic Phospholipid Signaling Modulates Axon Outgrowth via Glutamate-dependent Ca2 + -mediated Molecular Pathways. Cerebral Cortex 2017, 27:131–145.
4. Tsodyks M, Pawelzik K, Markram H. Neural networks with dynamic synapses
Neural Comput 1998, 10(4):821–35.
5. Mongillo G, Hansel D, van Vreeswijk C. Bistability and spatiotemporal
irregularity in neuronal networks with nonlinear synaptic transmission. Phys
Rev Lett 2012, 108(15):158101.
P266
Postsynaptic Activity‑Dependent Synaptic Scaling Enables the
Functional Organization of Memories
Juliane Herpich1,2, Christian Tetzlaff1,2
1
Third Institute of Physics - Biophysics, Department of Computational
Neuroscience, University of Goettingen, Goettingen, Germany; 2Bernstein
Center for Computational Neuroscience, University of Goettingen,
Goettingen, Germany
Correspondence: Juliane Herpich (juliane.herpich@phys.uni-goettingen.de)
BMC Neuroscience 2017, 18 (Suppl 1):P266
As known from everyday life, humans are permanently exposed to a
variety of sensory inputs from their environment. Thereby, the ongoing
challenge, humans have to deal with, is to continuously and adaptively
respond to these sensory stimulations. On the neuronal level, modification of synapses (interface between two neurons) is a weighty mechanism for adapting the response properties of neurons according to
their external stimulation. Hereby, synaptic plasticity is the main mechanism underlying learning [1–4] and, in combination with a homeostatic mechanism, yields the formation of strongly interconnected
subgroups of neurons [5–7], so-called Hebbian cell assemblies (CAs) [1].
Such a CA represents the learned memory trace of the corresponding
environmental stimulus [1]. Moreover, dependent on the details of the
stimuli, humans exhibit the remarkable ability to organize memories
Page 151 of 176
(i.e. CAs), thus, to connect, generalize, and discriminate them, which
supports the integration of novel stimuli and enables complex behavior [8, 9]. How these memory organizations are realized on a neuronal
level based on the idea of cell assemblies is still unknown.
Here, we analyze in a theoretical neuronal network model whether the
interaction of synaptic plasticity with different formulations of synaptic scaling [10] fulfills basic requirements of single synapse dynamics,
such as sensitivity to stimulations and stability in their weight dynamics. Our analyses show that synaptic plasticity in combination with
synaptic scaling dependent on the local (synapse specific) synaptic
weight and the global (dendritic-branch specific) postsynaptic activity suffices the aforementioned characteristics of synapse dynamics.
For simplicity, we abstract the neuronal dynamics of the network to
its respective dynamics in a mean-field model of two interconnected,
homogeneous populations of neurons. These populations serve as
memory representations on the neuronal level (i.e. CAs; strong synaptic weights). Given different stimulation protocols, these two CAs can
be dynamically connected with each other. Our analyses show that,
dependent on the stimulation protocol, the CAs can be associated,
discriminated, or can form a sequence. Remarkably, this rich repertoire
of memory interactions is only present if the CAs themselves are in a
matured state, in other words, if the timescale of the synaptic dynamics within the cell assemblies are slower than between them. This indicates that the interaction between memory items strongly depends
on the internal state of the cell assemblies involved.
In summary, this work reveals a neuronal network model that is capable to exhibit with a local (Hebbian synaptic plasticity and synaptic
weight-dependent scaling) and global (postsynaptic activity-dependent scaling) acting learning rule different functional organizations of
memories observed in human behavior.
References
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neuroscience, 3:1178–1183, 2000.
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combination with many generic plasticity mechanisms stabilizes circuit
connectivity. Frontiers in Computational Neuroscience, 5:47, 2011.
7. Tetzlaff C., Kolodziejski C., Timme M., Tsodyks M., and Wörgötter F.: Synaptic
scaling enables dynamically distinct short- and long-term memory formation. PLoS Computational Biology, 9(10):e1003307, 2013.
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9. Tulving E., and Donaldson W.: Organization of memory. 1972.
10. Turrigiano G.G., Leslie K.R., Desai N.S., Rutherford, L.C., and Nelson, S.B.:
Activity-dependent scaling of quantal amplitude in neocortical neurons.
Nature, 391(6670):892–896, 1998.
P267
Input‑dependent Synaptic Consolidation of Memory
Representations
Jannik Luboeinski1, Christian Tetzlaff2
1
Third Institute of Physics - Biophysics, Department of Computational
Neuroscience, University of Göttingen, Göttingen, Germany; 2Bernstein
Center for Computational Neuroscience, University of Göttingen,
Göttingen, Germany
Correspondence: Jannik Luboeinski (jannik.luboeinski@uni-goettingen.
de)
BMC Neuroscience 2017, 18 (Suppl 1):P267
Long-term synaptic plasticity serves as the main process underlying
learning and forming sustained memory representations in neuronal
networks. In conjunction with homeostatic plasticity, long-term synaptic
plasticity leads to the formation of strongly interconnected subgroups
BMC Neurosci 2017, 18(Suppl 1):60
of neurons [1, 2]. These subgroups are referred to as memory representations or Hebbian cell assemblies [3]. Experiments have identified two
different types of long-term synaptic plasticity [4, 5, 6, 7]: On the one
hand, early-phase plasticity acts on a time scale of a few hours, while, on
the other hand, late-phase plasticity operates on a time scale of more
than eight hours up to several days. The transition of a synapse from the
early-phase to the late-phase state is called synaptic consolidation and
requires protein synthesis in the soma of the postsynaptic neuron. These
proteins are transported along the dendrite and lead to a state-switch of
those synapses that have formed a tag beforehand (synaptic tagging and
capture hypothesis [2]). Protein synthesis as well as the formation of tags
depend on the ongoing neuronal activity and external stimuli. However,
the impact of synaptic consolidation on the formation and maintenance
of cell assemblies, thus on their stability, is still widely unknown.
Here, we investigate in a theoretical network model under which input
conditions synaptic consolidation yields the stabilization of cell assemblies, transferring the corresponding synapses from the early-phase
to the late-phase state. For this examination, we developed and analyzed a spiking network model based on a well-known single-synapse
model of the processes of synaptic consolidation [8, 9, 10]. Interestingly, our results show that the dynamics of synaptic consolidation are
not homogeneous within a cell assembly: namely, the system shows
a discrimination between the induction of late-phase plasticity and
early-phase plasticity. In the ‘core’ of the cell assembly, synapses are
in the long-living late-phase state, whereas in the surroundings of the
core (‘halo’), synaptic changes do not overcome the early phase. Further analysis indicates that this discrimination could imply functionally
important principles. In summary, our work provides a further step in
understanding the step-by-step consolidation of memory representations in biologically realistic neuronal networks.
References
Page 152 of 176
Working memory (WM) refers to the ability of humans and animals
to store as well as to process the continuously incoming stream of
stimuli and information on short time scales [1]. The neuronal dynamics implementing these two core functions of WM, to store and to process information, are still a matter of debate. In particular, it is unclear
whether working memory relies on attractor dynamics [2] or whether
it is realized by transient dynamics [3]. Several pieces of experimental
evidence as well theoretical considerations provide support for both
of these seemingly contradictory hypotheses.
Here, we approach this debate by considering the unreliability of
the timing of the stimuli received by the WM. Quite obviously, when
interacting with the environment, subjects cannot rely on precisely
timed input stimuli. The consequence of unreliability of input stimuli
on the operation of WM has been psychologically studied on subjects
performing the N-back task. It has been found that, in this task, introducing unpredictability of the occurrence timing of the stimuli does
not significantly influence the subject’s performance [4]. Based on this
finding, we investigate which kind of neuronal dynamics enables a
network to perform the N-back task with a comparable level of robustness with respect to variances in the stimuli timing.
The most widely used network model of transient neuronal dynamics
is the framework of reservoir networks [5, 6]. We test the performance
of reservoir networks trained with different learning algorithms and
with different feedback topologies on the N-back task. Interestingly, we
find that introducing already small variations in the timing of the input
stimuli reduces the performance of reservoir networks in the N-back
task significantly. We show that the performance can be restored by
explicitly training the network to represent past input stimuli via the
activity states of feedback loops. As this, in turn, effectively introduces
attractor states into the network, we conclude that only by exploiting
the properties of both, attractor states as well as of transient dynamics, a neuronal network is able to achieve a performance comparable to
the one found in working memory experiments. Task-relevant information is stored in attractor states and processing of information is accomplished by transient dynamics. As a consequence, we predict that in the
N-back task, an explicit recall stimulus should avoid a drop in performance resulting from introducing delays between the current stimulus
and the execution of the respective action. Thus, we provide an experimentally verifiable hypothesis about the underlying dynamics of WM
ruling out pure transient reservoir networks as a plausible model.
1. Tetzlaff C, Kolodziejski C, Timme M, Wörgötter F: Synaptic scaling in
combination with many generic plasticity mechanisms stabilizes circuit
connectivity. Frontiers in Computational Neuroscience 2011, 5:47.
2. Tetzlaff C, Kolodziejski C, Timme M, Tsodyks M, Wörgötter F: Synaptic scaling enables dynamically distinct short- and long-term memory formation.
PLoS Computational Biology 2013, 9:e1003307.
3. Hebb DO: The Organization of Behaviour, 1st Edition. New York: Wiley; 1949.
4. Abraham WC: How long will long term potentiation last? Phil. Trans. R. Soc. B
2003, 258:735–744.
5. Frey U, Morris R: Synaptic tagging and long-term potentiation. Nature 1997,
385:533–536.
6. Redondo R, Morris RGM: Making memories last: the synaptic tagging and
capture hypothesis. Nat. Rev. Neurosci. 2011, 12:17–30.
7. Sajikumar S, Navakkode S, Frey JU: Identification of compartment- and process-specific molecules required for “synaptic tagging” during long-term
potentiation and long-term depression in hippocampal CA1. J. Neurosci.
2007, 27:5068–5080.
8. Barrett AB, Billings G, Morris RGM, van Rossum MC: State based model of
long-term potentiation and synaptic tagging and capture. PLoS Comput.
Biol. 2009, 5:e1000259.
9. Clopath C, Ziegler L, Vasilaki E, Büsing L, Gerstner W: Tag-trigger-consolidation: a model of early and late long-term potentiation and depression. PLoS
Comput. Biol. 2008, 4:e10000248.
10. Li Y, Kulvicius T, Tetzlaff C: Induction and consolidation of calcium-based
homo- and heterosynaptic potentiation and depression. PLoS One 2016,
11:e0161679.
1. Baddeley AD: Working memory: theories, models, and controversies. Annu
Rev Psychol 2012, 63:1–29.
2. Riley MR, Constantinidis C: Role of prefrontal persistent activity in working
memory. Front Syst Neurosci 2016, 9:181.
3. Barak O, Sussillo D, Romo R, Tsodyks M, Abbot LF: From fixed points to
chaos: three models of delayed discrimination. Prog Neurobiol 2013,
103:241–222.
4. Koppe G, Gruppe H, Sammer G, Gallhofer B, Kirsch P, Lis S: Temporal
unpredictability of a stimulus sequence affects brain activation differently
depending on cognitive task demands. Neuroimage 2014, 101:236–244.
5. Jaeger H: The “echo state” approach to analyzing and training recurrent
neural networks. Tech. Rep., GMD - German National Research Institute for
Computer Science; 2001.
6. Maass W, Natschläger T, Markram H: Real-time computing without stable
states: a new framework for neural computation based on perturbations.
Neural Comput 2002, 14:2531–2560.
P268
Why working memory is not a reservoir: the role of transient
dynamics and attractors when processing unreliably timed inputs
Timo Nachstedt1,2, Christian Tetzlaff1,2
1
Third Institute of Physics, Universität Göttingen, Göttingen, 37077,
Germany; 2Bernstein Center for Computational Neuroscience, Göttingen,
37077, Germany
Correspondence: Timo Nachstedt (timo.nachstedt@phys.uni-goettingen.de)
BMC Neuroscience 2017, 18 (Suppl 1):P268
P269
Application of spike train synchrony measure Spike‑contrast
to quantify the effect of bicuculline on cortical networks grown
on microelectrode arrays
Manuel Ciba1, Andreas Bahmer2 and Christiane Thielemann1
1
Biomems lab, Faculty of Engineering, UAS Aschaffenburg, 63743
Aschaffenburg, Germany; 2Comprehensive Hearing Center, University
ENT-Clinic Würzburg, 97080 Würzburg, Germany
Correspondence: Manuel Ciba (manuel.ciba@h-ab.de)
BMC Neuroscience 2017, 18 (Suppl 1):P269
References
BMC Neurosci 2017, 18(Suppl 1):60
In neurosciences, it is assumed that neuronal information is mainly
coded in the timing of spikes which is why spike trains are typically the
data base for further analyzing procedures [1]. Synchrony is an important parameter since it is related to basic brain functions [2, 3] as well
as to pathological states [4]. In order to quantify synchrony between
two or more spike trains, several methods have been established,
being either time scale dependent or time scale independent.
The use of a new time scale independent and multivariate synchrony
measure is proposed, called Spike-contrast, with the aim to apply it on
spike train data sets recorded from cortical networks with a biochemically induced synchrony increase. The histogram based approach, calculates a visual contrast of a raster plot across different time scales to
quantify synchrony and is computational efficient when dealing with
large number of parallel spike trains, which makes it suitable for the
analysis of big data volumes recorded from high-density microelectrode arrays (MEA) Although its basic principal is different to existing
time scale independent measures like SPIKE-distance [5], synchrony
values of Spike-contrast and SPIKE-distance show a high correlation for
test data from Poisson spike models and Izhikevich networks. However, their results diverge, when applied to spike train data containing
synchronized bursts made of non-synchronized spikes: Whereas SPIKEdistance considers all time scales equally important [6] (and therefore
is also sensitive to non-synchronized spikes in bursts), Spike-Contrast
considers such spike trains perfectly synchronized.
The reflection of larger time scales in the range of burst duration has
been suggested before by [7, 8] to analyze bicuculline induced synchrony modification. Here, the new algorithm Spike-contrast, prioritizing large time scales, shall be applied on experimental data recorded
from cortical rat neurons grown on microelectrode arrays (MEA). Signals were recorded in a control situation and with bicuculline (10 µM), a
well-known drug that blocks the inhibitory action of GABAA receptors.
We find that Spike-contrast is able to significantly quantify the increase
in synchrony induced by bicuculline. Statistical significance is higher
than from other synchrony measures like SPIKE-distance, suggesting
that the bicuculline mediated synchrony increase is more distinct in
larger time scales and that Spike-contrast is an appropriate synchrony
measure for spike trains taken from experimental data, e.g. in biosensor applications [9].
References
1. Rieke F: Spikes: exploring the neural code. MIT press, 1999.
2. Engel AK, Fries P, Singer W: Dynamic predictions: oscillations and synchrony in
top–down processing. Nature Reviews Neuroscience, 2(10):704–716, 2001.
3. Ward LM: Synchronous neural oscillations and cognitive processes. Trends
in cognitive sciences, 7(12):553–559, 2003.
4. Uhlhaas PJ, Singer W: Neural synchrony in brain disorders: relevance for
cognitive dysfunctions and pathophysiology. Neuron, 52(1):155–168, 2006.
5. Kreuz T, Chicharro D, Houghton C, Andrzejak RG, Mormann F: Monitoring
spike train synchrony. Journal of neurophysiology, 109(5):1457–1472,
2013.
6. Satuvuori E, Mulansky M, Bozanic N, Lenk K, Kreuz T: Measures of spike
train synchrony for data with multiple time-scales. arXiv preprint
arXiv:1702.05394, 2017.
Page 153 of 176
7. Selinger JV, Pancrazio JJ, Gross GW: Measuring synchronization in neuronal
networks for biosensor applications. Biosensors and Bioelectronics,
19(7):675–683, 2004.
8. Chiappalone M, Vato A, Berdondini L, Koudelka-Hep M, Martinoia S: Network
dynamics and synchronous activity in cultured cortical neurons. International journal of neural systems, 17(02):87–103, 2007.
9. Flachs D, Ciba M: Cell-based sensor chip for neurotoxicity measurements in
drinking water. Lékař a Technika - Clinician and Technology, 46(2):46–50, 2016.
P270
Cortical states affect the optimal linear readout of network
dynamics
Eric S. Kuebler1, Joseph S. Tauskela2, & Jean-Philippe Thivierge1
1
School of Psychology, Faculty of Social Science, University of Ottawa,
Ottawa, Ontario, Canada; 2Human Health Therapeutics, National Research
Council of Canada, Ottawa, Ontario, Canada
Correspondence: Eric S. Kuebler (ekueb021@uottawa.ca)
BMC Neuroscience 2017, 18 (Suppl 1):P270
Spontaneous neuronal activity in vitro is often characterized by network bursts, whereby a large proportion of neurons are active in close
temporal contiguity. How this network state affects the optimal linear readout of neuronal dynamics remains an unresolved question
in neuroscience. Here, we recorded from dissociated cortical neurons
using multi-electrode arrays (MEAs) and computed a ‘spike-triggered
average’ of population activity for each electrode (N = 59), by measuring the probability of co-occurrences between the spiking activity on
the electrode of interest and that recorded on each of the other electrodes, termed the ‘preferred network state’. Results show that despite
fluctuations in spontaneous activity over time, population activity
over all electrodes can be described by a low-dimensional attractor
with N-1 parameters, substantially fewer than the number of parameters required for pairwise correlations (N2). To test whether activity
across different networks could be accurately discriminated, preferred
network states from the first half of recordings (10 min) were fed into
a linear model trained with a Fisher criterion. Then, the model was
tested by presenting it spiking activity from six networks sequentially.
We found that this model was useful in successfully discriminating
amongst different networks with less than 3% error rate (Figure 1A).
Further, the linear model was robust to adjustments in the number of
electrodes included for input to the LDA (Figure 1B), suggesting that
fewer than N-1 parameters can be useful to accurately discriminate
between networks. Using simulations of neural activity in a branching
model, we show that network activity near a critical regime (but not
necessarily at the exact critical point) is optimally discriminated by a
linear readout.
Conclusion: Overall, our results point to a dynamical signature for
representations of cortical activity whereby states near the critical
point are most amenable to decoding by linear downstream structures. Computation in the brain may occur by distributed processing
whereby several subnetworks are each responsible for contributing to
dynamical neuronal representations.
BMC Neurosci 2017, 18(Suppl 1):60
Page 154 of 176
◂
Figure 1. A. Preferred network states of several networks, denoted
by colour. Top panel: ground truth data. Bottom panel: predictions
based on the linear model. B. Correct rate of classification as a
function of the number of electrodes used for input
P271
Workflow, data format and tools to register neuron morphologies
to a reference brain atlas
Rembrandt Bakker1,2, María García-Amado3, Marian Evangelio3, Francisco
Clascá3, Paul Tiesinga1
1
Neuroinformatics department, Donders Institute for Brain, Cognition
and Behaviour, Radboud University Nijmegen, Nijmegen, The
Netherlands; 2Institute of Neuroscience and Medicine (INM-6)
and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute
I, Jülich Research Centre, Jülich, Germany; 3Departamento de Anatomía,
Histología y Neurociencia, Facultad de Medicina, Universidad Autónoma
de Madrid, Madrid, Spain
Correspondence: Rembrandt Bakker (r.bakker@donders.ru.nl)
BMC Neuroscience 2017, 18 (Suppl 1):P271
Neuronal reconstructions are essential building blocks for neuronal tissue
simulation at subcellular resolution. Several databases with thousands of
reconstructed morphologies exist, the largest being NeuroMorpho.Org
[1]. In this database, the location of each neuron is described in terms of
species and brain region. For a neuronal tissue builder like the one created in the Human Brain Project (HBP), this information is too coarse.
Only neurons that are registered into a spatial reference system can be
used. Here we present a workflow and accompanying tools to do this,
based on the manual annotation of a few points on the neuron.
Our use-case is the atlas registration of long-range projection neurons in mouse, which is particularly challenging since the neurons run
across many brain regions. The steps of the workflow are:
1. The experimental lab uses the widely used Neurolucida (MBF BioScience) software to reconstruct the neuron. For a small set of
points, the location is eye-balled in the Franklin-Paxinos mouse
brain atlas [2], and added to the reconstruction as a marker point.
2. The Neurolucida file (choice of binary, asci, xml) is read by a
newly written open-source parser (https://www.npmjs.com/
package/morphology_io), and converted to a newly developed
SWC + format (https://github.com/HumanBrainProject/swcPlus), an extension of the widely used SWC format [3].
3. The markers with atlas coordinates are extracted from the
SWC + file by a python script and used to estimate an affine transformation that maps the local coordinate system to the reference
space. The transformation parameters are saved to the SWC + file.
4. The Morphology Viewer [4], a new web-based morphology suite,
recognizes the transformation parameters in the file, and displays the
neuron along with sections from the atlas, see screenshot in Fig. 1.
In the use-case, 50 points were manually mapped to the atlas. To accelerate the procedure, this can be reduced to about 10 points. An alternative approach to this workflow is investigated in sub-project 5 of the
HBP. It uses a set of tissue-sections from which the neuron was reconstructed to semi-automatically find a similar location in the reference
atlas using the AligNII tool (http://www.nesys.uio.no/AligNII/).
Figure 1. Registered neuron with soma and dendrites (red) in
region LPLR and the main axonal arbor (blue) in area V1
BMC Neurosci 2017, 18(Suppl 1):60
Acknowledgements
Supported by the European Union Seventh Framework Programme
(FP7/2007-2013) under grant agreement numbers 604102 (HBP RUP)
and 720270 (HBP SGA1).
References
1. Halavi M, Polavaram S, Donohue DE, et al.: NeuroMorpho.org implementation of digital neuroscience: dense coverage and integration with the NIF.
Neuroinformatics 2008, 6(3):241–252. doi:10.1007/s12021-008-9030-1.2.
2. Paxinos G, Franklin K: the Mouse Brain in Stereotaxic Coordinates, 4th Edition.
Academic Press; 2013.
3. Cannon RC, Turner DA, Pyapali GK, Wheal HV: An on-line archive of reconstructed hippocampal neurons.
J Neurosci Methods 1998, 84(1–2):49–54. doi: 10.1016/S0165-0270(98)00091-0.
4. HBP Neuron Morphology Viewer [http://neuroinformatics.nl/HBP/
morphology-viewer/]
P272
Simple models of closed‑loop cortical‑environment interactions
Christopher L. Buckley1, Taro Toyoizumi2
1
Informatics, University of Sussex, Brighton, BN1 9RH, UK; 2Brain Science
Institute, RIKEN, Wako, Saitama. 351-0106, Japan
Correspondence: Christopher L. Buckley (c.l.buckley@sussex.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P272
Many investigations of the neural circuits underlying behaviour have
commonly started from the assumption that the brain is an input/output device. On this view, the brain operates in an open-loop, mapping
sensory input caused by the environment (exafferent input) to appropriate motor output. However, during active behaviour (e.g., running, whisking, swimming) sensory input is directly shaped by motor
actions and sensory perceptions inform future motor commands
forming a closed-loop between the brain and environment, see Fig. 1.
Page 155 of 176
The onset of active behaviours coincides with marked changes
in cortical dynamics. Typically, synchronous fluctuations of neural activity are strongly modulated by [2–4]. Further active behaviours shape neuronal responses. For example, the onset of running
sharpens response is visual cortex [4] but suppresses responses in
auditory cortex [3]. In the barrel cortex responses to brief whisker
perturbations are suppressed by whisking but responses to active
touch events (when the whisker is actively driven into an object) are
enhanced [5]. By analysing simple dynamical models, we examine to
what extent these phenomena can be accounted for by the closedloop feedback circuits necessary for active behaviour, see Fig. 1. In
particular, we argue that the onset of these feedback loops suggests
a parsimonious account of the changes to synchronous neuronal
fluctuations and sensory responses caused by the presence of active
behaviour and can account for mismatch responses caused by the
interruption of environmental feedback. Lastly we discuss the development of an experimental setup to test these ideas that utilizes
closed-loop interactions in larval zebrafish behaving in a closedloop virtual reality.
References
1. von Holst E. Relations between the central Nervous System and the peripheral organs. The British Journal of Animal Behaviour 1954, 2:89–94.
2. Crochet S, Petersen CCH. Correlating whisker behavior with membrane
potential in barrel cortex of awake mice. Nat Neurosci. 2006, 9: 608–610.
3. Schneider DM, Nelson A, Mooney R. A synaptic and circuit basis for corollary discharge in the auditory cortex. Nature 2014, 513: 189–194.
4. Niell CM, Stryker MP. Modulation of visual responses by behavioral state in
mouse visual cortex. Neuron. 2010, 65: 472–479
5. Crochet S, Poulet JFA, Kremer Y, Petersen CCH. Synaptic mechanisms
underlying sparse coding of active touch. Neuron 2011, 69:1160–1175.
P273
Encoding multiple spaces in grid‑cells networks
Alexis M. Dubreuil1, Rémi Monasson1, Alessandro Treves2
1
Laboratoire de Physique Théorique, Ecole Normale Supérieure, Paris,
France; 2Cognitive Neuroscience, SISSA, Trieste, Italy
Correspondence: Alexis M. Dubreuil (alexis.dubreuil@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P273
Figure 1. A schematic of brain/body/environment interaction
during active behaviour. Motor actions on the body/environment
impact on sensory areas as reafferent input (black arrow) which is
combined with exafferent input (brown arrows) to form the sensory stream. Motor areas also send efferent signals, or corollary
discharges, to sensory systems (magenta arrows). Further during active behavior sensory input also informs motor output (red
arrow). Thus, neuronal dynamics in sensory areas are affected by
three forms of feedback: environmental feedback (yellow dashed),
internal sensor/motor feedback (blue dashed) and endogenous
recurrent feedback (cyan dashed)
Grid-cells found in entorhinal cortex form a representation of
space through their receptive fields that pave space with triangular lattices. It has been proposed that the firing of grid-cells with
similar grid spacing is supported by recurrent connectivity, such
that network dynamic lies on a 2-D manifold of stable states [1].
Recent empirical evidence suggests that the grid-cell code is not
limited to physical space encoding in entorhinal cortex, but could
also subserve encoding of more abstract spaces in other cortical
areas [2]. In order to assess the efficiency of neural networks in
using a grid-cell code, the present study is focused on the problem of counting the number of grid-like manifolds that can be reliably embedded in a neural network, this number being referred to
as the storage capacity of the network. We consider a network of
binary neurons in which each manifold is imprinted in the connectivity matrix via a Hebbian component (neurons with similar receptive fields in a manifold are connected). We compute the number
of stable manifolds by extending replica calculations developed in
[3] for a model of place cells. Such analytical calculations allow us
to explore the performance of various grid-cell codes. We focus on
two characteristics of such codes. First, we explore how the typical
paving of space in a manifold impact network’s storage capacity
(see Figure 1 A and B). Second we explore how the storage capacity depends on the parameters of the Hebbian rule shaping the
connectivity matrix. Overall, our study suggests that a grid-cell
code can make an efficient use of neural resources since up to 200
stable manifolds can be imprinted in the connectivity matrix of a
network of 10,000 neurons (see Figure B), roughly the size of a cortical column.
BMC Neurosci 2017, 18(Suppl 1):60
Page 156 of 176
storage load. This result holds both for a sparse activity model suitable
for a plausible description of CA3 place cells, and for a toy model version with periodic boundary condition on a two-dimensional torus.
Imposing now periodic hexagonal boundary condition on the connectivity allows us to model grid cell like behavior, and to calculate the
regime in which the system can retrieve and/or maintain a representation of position in one of the stored environments. Surprisingly, the
storage capacity appears to be very much higher than in the “square”
toy model, by several orders of magnitude.
In graded-response networks, however, mixed states of multiple
attractors (corresponding to recall of multiple environments) are
known to also exhibit stability, even though in a limited region of
parameter space [2].
Also in our “hexagonal continuous attractor” network we find, in addition to the “classical” retrieval behavior, a regime in which external
cues trigger retrieval in more than one map simultaneously, in a similar way as described in [3] for two 1D periodic manifolds.
Our study generalizes these results to 2-dimensional uncorrelated maps,
and extends them to the case with a large number of stored maps.
References
Figure 1. A. Example of the receptive field of a neuron that paves
the 2-D flat space. We implement different kinds of paving, for
instance a square lattice (left), or a hexagonal lattice (right) as
those found in entorhinal cortex. B. Storage capacity of the network as a function of the amount of thermal noise T in the network’s dynamics. For a given T, the lines give the maximal number
of stable manifolds the network can support, given the number of
neurons it is composed of
References
1. Burak Y, Fiete IR: Accurate path integration in continuous attractor network
models of grid cells. PLoS Computational Biology 2009, 5(2): e1000291.
doi:10.1371/journal.pcbi.1000291
2. Constantinescu AO, O’Reilly JX, Behrens TEJ: Organizing conceptual knowledge in humans with a gridlike code. Science 2016, 352(6292):1464–1468.
3. Monasson R, Rosay S: Crosstalk and transitions between multiple spatial
maps in an attractor neural network model of the hippocampus: Phase
diagram. Phys Rev E 2013, 87:062813.
P274
Storage capacity of threshold‑linear networks for gridlike
continuous attractors
Davide Spalla, Sophie Rosay, Alessandro Treves
Cognitive Neuroscience Sector, SISSA, Trieste, 34136, Italy
Correspondence: Davide Spalla (dspalla@sissa.it)
BMC Neuroscience 2017, 18 (Suppl 1):P274
Attractor neural networks play an important role in modeling the
mechanisms of spatial memory, allowing for analytical and computational analyses of the recurrent circuitry of hippocampal and cortical networks known to be involved in the cognitive representation of
space.
Here we study a recurrent network of threshold-linear neurons, and its
capacity for storing multiple spatial maps as continuous attractors.
In this model, storage capacity can be analytically evaluated in the
mean field approximation, as shown in [1].
The existence of a retrieval phase reduces to the existence of the solutions of a single equation, which disappear at a critical value of the
1. Battaglia F, Treves A: Attractor neural networks storing multiple space representations: A model for hippocampal place fields. Physical Review E. 1998,
58(6):7738.
2. Roudi Y, Treves A: Disappearance of spurious states in analog associative
memories. Phys Rev E Stat Nonlin Soft Matter Phys. 2003, 67(4 Pt 1):041906.
3. Romani S, Tsodyks M: Continuous attractors with morphed/correlated
maps. PLoS Comput Biol. 2010; 6(8)
P275
A network model of the turnover dynamics of excitatory
and inhibitory synapses in cortex
Florence I. Kleberg, Jochen Triesch
Neuroscience Lab, Frankfurt Institute for Advanced Studies, Frankfurt am
Main, Hessen, 60438, Germany
Correspondence: Florence I. Kleberg (kleberg@fias.uni-frankfurt.de)
BMC Neuroscience 2017, 18 (Suppl 1):P275
It has long been known that there is substantial turnover of excitatory
synapses in cortex during both development and adult life [1]. Recent
experiments using markers for GABAergic synapses have shown that
inhibitory synapses are also highly dynamic [2–3]. Specifically, inhibitory synapses exhibit approximately exponentially decreasing survival
fractions and show reduced lifetimes when sensory input is decreased
[3]. Here we show that such dynamics of excitatory and inhibitory synapses result from a combination of structural plasticity, Spike-Timing
Dependent Plasticity (STDP), and multiplicative normalisation in a
Self-Organizing Recurrent Neural Network (SORN; [4]) of Leaky Integrate-and-Fire neurons with membrane noise and external Poisson
inputs. Both synapse types are grown from an initially unconnected
network state by random synapse creation. Synapses whose efficacies fall below a threshold are pruned. We find that excitatory and
inhibitory synaptic weights develop lognormal-like distributions as
observed experimentally [2] and that the lifetimes of synapses follow
a power law-like distribution. Furthermore, we find that the fraction
of surviving inhibitory synapses decays approximately exponentially
as observed experimentally ([3]; Figure 1A) and is modulated by the
strength of potentiation (LTP) and depression (LTD) in the inhibitory
STDP rule. Finally, depriving the network of external input decreases
the survival fraction of inhibitory synapses as observed in vivo ([3];
Figure 1A). To gain deeper insight into the underlying mechanisms we
formulate a statistical model of the time evolution of synaptic efficacies and find that it well describes the power law-like lifetimes and
exponential-like decreasing survival fractions (Figure 1B). We conclude
that the experimentally observed turnover dynamics of inhibitory synapses can be explained by local, biologically plausible plasticity mechanisms and are well described by a simple stochastic model.
BMC Neurosci 2017, 18(Suppl 1):60
Figure 1. A. Survival fractions of inhibitory synapses in the SORN
are shown by the solid grey lines. Light grey, with external input
to the network. Dark grey, without external input. The exponential
fit is shown by the dotted line. B. Survival fractions of weights in
a simple stochastic model are shown by the solid grey line. The
exponential fit is shown by the dotted line. The time scale of plasticity in the simulations in A and B is sped up compared to experimental findings in order to save computation time
References
1. Berry KP, Nedivi E: Experience-dependent structural plasticity in the visual
system. Annu. Rev. Vis. Sci. 2016, 2:17–35.
2. Rubinski A, Ziv NE: Remodeling and Tenacity of Inhibitory Synapses: Relationships with Network Activity and Neighboring Excitatory Synapses. PLoS
Comput Biol 2015, 11(11): e1004632.
3. Villa KL, Berry KP, Subramanian J, Cha JW, Oh WC, Kwon H, Kubota Y, So PTC,
Nedivi E: Inhibitory Synapses are repeatedly assembled and removed at
persistent sites in vivo. Neuron 2016, 89(2):756–789.
4. Lazar A, Pipa G, Triesch J: SORN: a self-organizing recurrent neural network.
Front Comp Neurosci 2009, 3:23
P276
Retinal prostheses from stimulation to seeing
Willy Wong1,2, Bruno de Oliveira Floriano1, Toshihiko Matsuo3, Tetsuya
Uchida4
1
Dept. of Electrical and Computer Engineering, University of Toronto,
Toronto, ON, M5S3G4, Canada; 2Institute of Biomaterials and Biomedical
Engineering, University of Toronto, Toronto, ON, M5S3G9, Canada;
3
Ophthalmology, Okayama University Medical School, Okayama,
700-8558, Japan; 4Polymer Materials Science, Faculty of Engineering,
Okayama University, Okayama, 700-8530, Japan
Correspondence: Willy Wong (willy.wong@utoronto.ca)
BMC Neuroscience 2017, 18 (Suppl 1):P276
A major concern with the design of any prosthetic device is the need
for objective evaluation. Is there a way to better evaluate what the
person senses without resorting to subjective testing after implantation? This study addresses a new approach to retinal prosthetic design,
together with detailed modelling of retinal function to tackle the
question of what is a person actually “sees”. Retinitis pigmentosa and
age-related macular degeneration can lead to blindness due to loss
of the photoreceptor layer. The Okayama University-type Retinal Prosthesis (OUReP) is a new approach to retinal implant design which does
not involve stimulation via micro-electrode arrays [1]. Instead a thin
film consisting of photosensitive dye molecules attached to a polyethylene layer is placed at back of the eye. The dye is designed to respond
only in the visible wavelengths and generates an electric potential in
response to incident light likely due to the formation of dipoles. Currently OUReP has been successfully tested in rats with a next step in
rabbit implantation. A current problem is understanding how the dye
works to facilitate vision.
We have been studying the effect of the potential on the visual pathway by first modelling the physiological system as an electromagnetic
boundary value problem. The potential was solved computationally
in COMSOL to obtain the extracellular potential outside the retinal
cells. This is what generates activity in the retinal cells and ultimately
drives action potentials in the optic nerve. The neural activity was then
solved using Hodgkin-Huxley equations and the cable model with the
‘activating function’ [2] derived from the extracellular potential solved
Page 157 of 176
from Laplace’s equation. From this, we found that ganglion cells are
excited by stimulating the OUReP photosensitive dye layer at ambient
light levels.
The next step involves exploring the neural code behind ‘seeing’. A
theory of sensory information processing has been in development for
nearly 50 years [3] and provides, perhaps for the first time, an accurate
model of the firing rate response in primary afferent neurons [4]. This
approach is based on the entropy of the sensory signal and calculates
the response due to uncertainty associated with the input. The theory
has been shown to work well across different modalities as well as for
different animal species. When applied to the retina, a single equation
of 5 parameters together with a coupling equation provides a good
estimate of the potential of retinal bipolar cells, and also the average
spike rate response of retinal ganglion cells for both on-centre and
off-centre cells. The population of retinal cells is diverse, and it is not
likely that a simple model can encompass the entirety of the physiological response. However, this is a first step in understanding the
quantitative functioning of the retina. By combining the physiological
response solved through the activating function, and comparing this
with predictions from entropy theory, we can better estimate what
a person will see, and thus provide a more objective assessment of
implant function as well as guide future development.
Acknowledgements
This work was supported by a grant from Natural Science and Engineering Research Council of Canada to WW. BF acknowledges the support of Ciências sem Fronteiras.
References
1. Alamusi, Matsuo T, Hosoya O, et al. Vision maintenance and retinal apoptosis reduction in RCS rats with Okayama University-type retinal prosthesis
(OUReP™) implantation. J Artif Organs. 2015, 18(3):264–271.
2. Werginz P, Benav H, Zrenner E, Rattay F. Modeling the response of ON and
OFF retinal bipolar cells during electric stimulation. Vision Res 2015, 111(Pt
B):170–181.
3. Norwich, K.: Information, Sensation and Perception. New York: Academic
Press; 1993.
4. Wong W. On the Asymptotic, Near-Equilibrium Sensory Response. arXiv.
2013 :1307.6445
P277
Metabolic cost of neuronal oscillations
Domenica Dibenedetto1, Kâmil Uludağ1,2
1
Maastricht Centre for Systems Biology (MaCSBio), Maastricht University,
Maastricht, The Netherlands; 2Maastricht Brain Imaging Centre (MBIC),
Faculty of Psychology & Neuroscience, Maastricht University, Maastricht,
The Netherlands
Correspondence: Domenica Dibenedetto (domenica.dibenedetto@
maastrichuniversity.nl)
BMC Neuroscience 2017, 18 (Suppl 1):P277
The association between cognitive functions in humans, the increase
in glucose and oxygen utilization and the expression of energy
metabolism genes is an active area of research [1]. The human
brain accounts for at least 20% of the body’s energy consumption
[2]. Much of the brain’s energy use goes on to re-establish electrochemical gradients following action potentials and synaptic currents [3, 4]. The high energy demand at the synapse implies that
local mechanisms must exist to sense synaptic activity and provide
the energy substrates necessary to sustain pre- and postsynaptic
processes. Here, we investigate the relationship between ionic currents associated with neuronal activity at the synaptic site and the
brain energy consumption using both experimental data and mathematical models. Using a bottom-up approach, we model a recurrent
network of excitatory-inhibitory neurons, stimulated with realistic
dynamic inputs, in order to determine the metabolic costs of neuronal oscillations [5]. An important early finding from studies in rat
was that energy use by neurons (oxidative glucose consumption) is
linearly correlated to excitatory neuronal activity (glutamate release)
[6]. As first step, we are investigating how the various ionic currents
BMC Neurosci 2017, 18(Suppl 1):60
measured at the excitatory synaptic site respond at different frequency ranges of a square-pulsed input signal through a parameters
space exploration study. A morphologically and functionally realistic
pyramidal neuron is considered as postsynaptic compartment [7].
This new physiologically inspired conductance-based [4] neuronmodel can be the basis of a more complex network in order to monitor metabolism at micro-circuit level. The result of the modelling will
be linked to non-invasive neuroimaging modalities, such as fMRI and
EEG [8, 9], which are related to either the local metabolic costs of
neuronal activity or local synchronicity of the microcircuit. Studying
the mechanisms of brain metabolism is of great interest in order to
understand not only the fundamental physiological phenomena of
brain functions, but also the significance of alterations in functional
brain imaging signals detected in several neurodegenerative disorders affecting cognitive processes.
References
1. Magistretti, Pierre J. and I. Allaman, A Cellular Perspective on Brain Energy
Metabolism and Functional Imaging. Neuron. 86(4): p. 883–901.
2. Attwell, D. and S.B. Laughlin, An energy budget for signaling in the grey
matter of the brain. J Cereb Blood Flow Metab, 2001. 21(10): p. 1133–45.
3. Harris, Julia J., R. Jolivet, and D. Attwell, Synaptic Energy Use and Supply.
Neuron. 75(5): p. 762–777.
4. Hines, M.L. and N.T. Carnevale, The NEURON simulation environment.
Neural Comput, 1997. 9(6): p. 1179–209.
5. Mazzoni, A., et al., Understanding the relationships between spike rate
and delta/gamma frequency bands of LFPs and EEGs using a local cortical
network model. Neuroimage, 2010. 52(3): p. 956–72.
6. Sibson, N.R., et al., Stoichiometric coupling of brain glucose metabolism and
glutamatergic neuronal activity. Proc. Natl. Acad. Sci. U.S.A. 95, 316–321.
7. Hay, E., et al., Models of Neocortical Layer 5b Pyramidal Cells Capturing a
Wide Range of Dendritic and Perisomatic Active Properties. PLoS Comput.
Biol., 2011. 7, e1002107.
8. Raichle, M.E. and M.A. Mintun, Brain work and brain imaging. Annu Rev
Neurosci, 2006. 29: p. 449–76.
9. Logothetis, N.K., et al., Neurophysiological investigation of the basis of the
fMRI signal. Nature, 2001. 412(6843): p. 150–7.
P278
Multi‑cluster structure and dynamics in networks of coupled phase
oscillators through different classes of STDP profiles
Abdorreza Goodarzinick1, Mojtaba Madadi Asl1, Alireza Valizadeh1,2
1
Department of Physics, Institute for Advanced Studies in Basic Sciences
(IASBS), Zanjan 45137-66731, Iran; 2School of Cognitive Sciences, Institute
for Research in Fundamental Sciences (IPM), Tehran 19395-5746, Iran
Correspondence: Abdorreza Goodarzinick (a.goodarzinick@iasbs.ac.ir)
BMC Neuroscience 2017, 18 (Suppl 1):P278
In the brain networks, strength of synaptic connections is adjusted
according to relative spike timing of pre- and post- synaptic neurons, known as spike-timing-dependent plasticity (STDP) [1]. The
theoretical and simulational studies have shown different emergent collective activity and patterns of connectivity when using
different STDP profiles with different set of parameters. For example, depending on the parameter set, STDP can either promote or
oppose synchrony [2]. It also might lead to potentiation of bidirectional connections or eliminate two-neuron loops by depressing
one of the connections for every pair of reciprocally connected
neurons [3].
Here, we have inspected how different biologically observed STDP
profiles can result in different emergent dynamics. Based on Kuramoto
model for description of the phase dynamics in neuronal ensembles
[4, 5], we have designed classes of plasticity functions with symmetric and anti-symmetric profiles, mimicking typical forms of STDP for
excitatory and inhibitory connections (see Figure 1). Related sets of
differential equations for evolution of the phases and the synapses are
analytically solved to justify results of numerical simulations. The main
observation is that while anti-symmetric profile promotes one structural cluster with almost synchronized dynamics, symmetric profiles
lead to multi-cluster structure with various phase relations within and
between the clusters.
Page 158 of 176
Figure 1. Steady-state distribution of phase differences and connection strengths which are emerged through the function of 3
different sets of plasticity profiles. In the upper panels the plasticity
profile, distribution of phase lags and effective plasticity function
which is obtained by convolution of the phase lags and the plasticity profile are shown. The final pattern of the phase lags and the
synaptic weights between each pair of oscillators are shown in the
bottom panels
References
1. Bi GQ, Poo MM: Synaptic modifications in cultured hippocampal neurons:
dependence on spike timing, synaptic strength, and postsynaptic cell type.
J Neurosci 1998, 18(24): 10464–10472.
2. Lubenov E V, Siapas AG: Decoupling through synchrony in neuronal circuits
with propagation delays. Neuron 2008, 58:118–131.
3. Madadi Asl M, Valizadeh A, Tass PA: Dendritic and Axonal Propagation
Delays Determine Emergent Structures of Neuronal Networks with Plastic
Synapses. Sci Rep 2017, 7:39682.
4. Kuramoto, Y: Chemical oscillations, waves, and turbulence. Berlin: Springer;
1984.
5. Maistrenko YL, Lysyansky B, Hauptmann C, Burylko O, Tass PA: Multistability
in the Kuramoto model with synaptic plasticity. Phys Rev E 2007, 75:066207.
P279
Multi‑scale network structure of macaque visual cortex:
connectivity map, cortical architecture, and layer‑specific pathways
Maximilian Schmidt1,2, Rembrandt Bakker1,3, Claus C. Hilgetag4,5, Markus
Diesmann1,6,7, Sacha J van Albada1
1
Institute of Neuroscience and Medicine (INM-6) and Institute
for Advanced Simulation (IAS-6), Jülich Research Centre and JARA,
Jülich, Germany; 2Laboratory for Neural Circuit Theory, RIKEN Brain
Science Institute, Wako, Japan; 3Donders Institute for Brain, Cognition
and Behavior, Radboud University Nijmegen, Nijmegen, Netherlands;
4
Department of Computational Neuroscience, University Medical Center
Eppendorf, Hamburg, Germany 5Department of Health Sciences, Boston
University, Boston, MA, USA; 6Department of Psychiatry, Psychotherapy
and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen,
Germany; 7Department of Physics, Faculty 1, RWTH Aachen University,
Aachen, Germany
Correspondence: Sacha J van Albada (s.van.albada@fz-juelich.de)
BMC Neuroscience 2017, 18 (Suppl 1):P279
Extensive available axonal tracing data along with predictive connectomics allows a novel quantitative description of the network
structure of macaque cortex. Since the effects of connectivity on network dynamics are influenced by the size of cortical populations, and
since neuron density is predictive of connectivity [1, 2], it is relevant
to also characterize numbers of neurons when deriving a connectivity map. In this study, we integrate data on cortical architecture and
axonal tracing data into a multi-scale account of the network structure
of macaque vision-related cortex. The resulting connectivity map predicts the connection probability between any two neurons based on
their types, areas, and layers. Combining cell densities with published
micrographs provides a quantification of the reduction of relative layer
4 thickness with cell density from structurally differentiated to less
differentiated areas. Similarly, total cortical thickness decays with cell
density. Under the assumption of a relatively constant density of synapses, this yields denser connectivity in structurally less differentiated
BMC Neurosci 2017, 18(Suppl 1):60
areas. Combined anterograde and retrograde tracing data reveal that
synaptic target patterns of corticocortical connections depend on the
laminar origin of the projection in a manner that complements earlier
accounts of the association between source and target patterns [3, 4].
Statistically assigning synapses to target neurons based on dendritic
length in anatomical reconstructions [5] suggests that layer 4 neurons
receive non-negligible feedback. Our layer-specific connectivity map
enables a novel characterization of direct and polysynaptic pathways
through the network. It can be tested in simulations and experiments
whether these directionally specific paths open up channels for targeted corticocortical communication, akin to recently highlighted
hierarchically differential oscillatory interactions [6, 7].
Acknowledgements
Helmholtz Portfolio Supercomputing and Modeling for the Human
Brain (SMHB), European Union (BrainScaleS, grant 269921 and Human
Brain Project, grant 604102), the Jülich Aachen Research Alliance
(JARA), and the German Research Council (DFG grants SFB936/A1,Z1
and TRR169/A2).
References
1. Hilgetag CC, Medalla M, Beul SF, Barbas H: The primate connectome in
context: principles of connections of the cortical visual system. NeuroImage
2016, 134:685–702.
2. Beul SF, Barbas H, Hilgetag CC: A predictive structural model of the primate
connectome. arXiv preprint 2015, arXiv:1511.07222.
3. Felleman DJ, Van Essen DC: Distributed hierarchical processing in the
primate cerebral cortex. Cereb Cortex 1991, 1:1–47.
4. Markov NT, Ercsey-Ravasz M, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H: Cortical high-density counterstream architectures. Science 2013,
342:1238406.
5. Binzegger T, Douglas RJ, Martin KA: A quantitative map of the circuit of cat
primary visual cortex. J Neurosci 2004, 24:8441–8453.
6. Van Kerkoerle T, Self MW, Dagnino B, Gariel-Mathis MA, Poort J, Van Der Togt
C, Roelfsema PR: Alpha and gamma oscillations characterize feedback and
feedforward processing in monkey visual cortex. PNAS 2014, 111(40),
14332–14341.
7. Bastos AM, Vezoli J, Bosman CA, Schoffelen JM, Oostenveld R, Dowdall JR, De
Weerd P, Kennedy H, Fries P: Visual areas exert feedforward and feedback
influences through distinct frequency channels. Neuron 2015, 85: 390–401.
P280
The interaction of synaptic and structural plasticity in recurrent
networks
Michael Fauth, Mark van Rossum
School of Informatics, University of Edinburgh, Edinburgh, United
Kingdom
Correspondence: Michael Fauth (mfauth@gwdg.de)
BMC Neuroscience 2017, 18 (Suppl 1):P280
The connectivity of cortical networks determines the way they process information. Changes in the connectivity and, hence, in the information processing in response to certain stimuli are associated with
learning and memory formation [1, 2]. There are two classes of activity-dependent processes that change the connectivity of cortical networks: synaptic plasticity, which changes the transmission efficacies or
weights of the synapses, and structural plasticity, which creates and
removes synapses. These processes are strongly interacting, for example, the lifetime of synapses depends on their synaptic weight and correlated quantities as the volume of the corresponding dendritic spine
head [3, 4]. Hence, to understand how memories are stored in the connectivity, we analyze the interaction of synaptic and structural plasticity in recurrent networks. Moreover, we address the question how
memories stored by the connectivity can be maintained on timescales
of months and years, although the underlying synapses are removed
or exchanged on the timescale of days [4, 5].
Using mean-field analysis and simulations, we show that the synapses
in a population of recurrently connected neurons exhibit a collective dynamics which gives rise to two stable states: the population
can be either weakly interconnected or strongly interconnected with
synapses stabilizing each other. The population remains in its current
Page 159 of 176
state despite the creation or removal of individual synapses, such that
information about the population state can be retained much longer
than the lifetime of individual synapses. Moreover, the population can
be brought to either state by changing the input stimulation. These
results also extend to sub-populations of the network. For example,
when providing a small subset of neurons in a network with a higher
input current, this subset becomes highly interconnected, effectively
forming a Hebbian cell assembly [6–8].
Interestingly, this collective dynamics can be implemented independent of the bistability of neuronal activities controlled by synaptic
weights in recurrently connected populations, which has been proposed as a model of observed persistent activity [9]. As a consequence,
even at low activities, a (sub-)population can remain connected with
many synapses with relatively small weights. This, in turn, allows for
a rapid increase of these weights upon retrieval or relearning, which
might be related to Ebbinghaus’ savings phenomenon.
References
1. Yang G, Pan F, Gan WB: Stably maintained dendritic spines are associated
with lifelong memories. Nature 2009, 462: 920–924.
2. Xu T, Yu X, Perlik AJ, Tobin WF, Zweig JA, et al.: Rapid formation and selective stabilization of synapses for enduring motor memories. Nature 2009,
462:915–919
3. Yasumatsu, N, Matsuzaki, M, Miyazaki, T, Noguchi, J & Kasai, H: Principles of
Long-Term Dynamics of Dendritic Spines. J Neurosci 2008, 28:13592–13608
4. Loewenstein, Y; Yanover, U, Rumpel, S: Predicting the dynamics of network
connectivity in the neocortex. J Neurosci 2015, 35:12535–12544
5. Fauth, M., Wörgötter, F., Tetzlaff, C.: Formation and Maintenance of Robust
Long-Term Information Storage in the Presence of Synaptic Turnover. PLoS
Comput Biol 2015, 11:e1004684
6. Tetzlaff, C, Kolodziejski, C, Timme, M, Tsodyks, M, Wörgötter, F: Synaptic scaling enables dynamically distinct short- and long-term memory formation.
PLoS Comput Biol 2013, 9, e1003307
7. Litwin-Kumar, A, Doiron, B: Formation and maintenance of neuronal assemblies through synaptic plasticity. Nat Commun, 2014, 5:5319
8. Zenke, F, Agnes, EJ, Gerstner, W: Diverse synaptic plasticity mechanisms
orchestrated to form and retrieve memories in spiking neural networks
Nat. Commun 2015, 6:6922
9. Brunel, N: Persistent activity and the single-cell frequency-current curve in a
cortical network model. Network 2000, 11:261–280
P281
Automatic calibration for hybrid circuits of living and artificial
neurons
Manuel Reyes-Sánchez, Irene Elices, Rodrigo Amaducci, Carlos Muñiz,
Francisco B. Rodríguez, Pablo Varona
Grupo de Neurocomputación Biológica, Dpto. de Ingeniería Informática,
Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid,
Spain
Correspondence: Pablo Varona (pablo.varona@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P281
Hybrid circuits are networks built with living neurons and artificial
model (electronic or software) neurons and connections [1]. These
circuits allow characterizing neural network dynamics, probing circuit
function and have been used to assess the role of individual cells and
synapses in specific networks (e.g. see [2–6]).
The temporal scale and amplitude ranges of membrane voltage in
living neurons are in general different from the characteristic time
scales and amplitudes of the corresponding voltage variables in
the models, and thus in most cases signals from/to the living neuron and the model have to be scaled, and/or offset. Model integration time and real time deadlines to implement the hybrid circuit
closed-loop are also part of the problem. The process to calibrate
both amplitude and time domains is a main impairment in the construction of hybrid circuits. Manual calibration is often difficult and
entails a long time and damage risk, something critical due to the
limited survival time of the biological preparations and their low
resistance to current injection beyond unknown physiological limits. Recording drift is also an issue that has to be addressed in hybrid
configurations.
BMC Neurosci 2017, 18(Suppl 1):60
In this work, we present a protocol to perform automatic calibration
in hybrid circuits. The protocol is based on achieving a target synchronization level through an artificial electrical connection between
the living and the artificial neuron in a regime that guarantees active
generation of action potentials. Based on the synchronization criteria,
parameters of both temporal scale (model integration time and acquisition/stimulation time constraints) and amplitude scale (voltage and
current from/to the living neuron) are set automatically in just a few
seconds.
We illustrate our protocol by building a hybrid circuit in the pyloric
central pattern generator of Carcinus Maenas. The automatic calibration algorithm allows the construction of hybrid circuits in minutes.
By reducing calibration time and the risk of damaging the preparation, it is possible to extend the experimental time for the goal given
to the hybrid circuit, for instance the exploration of specific dynamical
regimes. The automatic search of model parameters in hybrid circuits
also allows tuning the best model configuration in the experiment.
The proposed algorithm can be easily generalized for any electrophysiological preparation.
Acknowledgements
We acknowledge support from MINECO/FEDER DPI2015-65833-P,
TIN2014-54580-R (http://www.mineco.gob.es/) and ONRG grant
N62909-14-1-N279.
References
1. Szucs A, Varona P, Volkovskii AR, Abarbanel HDI, Rabinovich MI, Selverston AI.
Interacting Biological and Electronic Neurons Generate Realistic Oscillatory
Rhythms. Neuroreport. 2000; 11:563–9.
2. Pinto RD, Varona P, Volkovskii AR, Szücs A, Abarbanel HDI, Rabinovich MI.
Synchronous behavior of two coupled electronic neurons. Phys. Rev. E.
2000; 62:2644–56.
3. LeMasson G, Masson SR-L, Debay D, Bal T. Feedback inhibition controls
spike transfer in hybrid thalamic circuits. Nature. 2002; 417:854.
4. Nowotny T, Varona P. Dynamic Clamp Technique. In: Jaeger D, Jung R, editors.
Encycl. Comput. Neurosci. Springer New York 2015.
p. 1048–51.
5. Hooper RM, Tikidji-Hamburyan RA, Canavier CC, Prinz AA. Feedback control
of variability in the cycle period of a central pattern generator. J. Neurophysiol. 2015; 114:2741–52.
6. Linaro D, Couto J, Giugliano M. Real-time Electrophysiology: Using Closedloop Protocols to Probe Neuronal Dynamics and Beyond. J. Vis. Exp. 2015;
100:e52320.
P282
Role of asymmetry in shaping spiking‑bursting activity of Central
Pattern Generators
Irene Elices1, David Arroyo1, Rafael Levi1,2, Francisco B. Rodriguez1, Pablo
Varona1
1
Grupo de Neurocomputación Biológica, Dpto de Ingeniería Informática,
Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid,
Spain; 2Department of Biological Sciences, University of Southern
California, Los Angeles, CA, USA
Correspondence: Irene Elices (irene.elices@uam.es)
BMC Neuroscience 2017, 18 (Suppl 1):P282
Motor rhythmic patterns in many biological systems are produced
by Central Pattern Generators. These networks are typically based
on reciprocal inhibitory subcircuits responsible for the production of
alternating spiking-bursting activity and the intrinsic dynamics of their
constituent cells [1, 2]. In many modelling studies, neurons are considered identical and the reciprocal inhibition, a hallmark of CPG connectivity, is frequently modeled as a symmetric interaction. In this study,
we emphasize the importance of asymmetry in the generation and
coordination of CPG rhythms from a computational and experimental
point of view.
The conductance-based models used in our computational study are
inspired by the crustacean pyloric CPG [3, 4]. In particular, the network
considered in this work is built up with four Hodgkin-Huxley type neurons and simplified versions of the known connection topologies of
the pyloric CPG. The chosen neuron model displays a wide dynamical
regime that includes irregular spiking-bursting modes similar to the
Page 160 of 176
observed behavior of CPG neurons in isolation. Using these models,
we studied the role of asymmetric maximal synaptic conductances,
time constants and gap-junction connectivity in the production of
regular and irregular bursting activity. Our results show that large
regions of both regular and irregular but coordinated rhythms exists
as a function of the asymmetry in the circuit. Both asymmetric maximal conductances and inhibitory synaptic time scales contribute to
the shaping of wide regimes of regular and irregular triphasic spikingbursting activity.
Our experimental results of irregular spiking-bursting activity in Carcinus maenas indicate the relevant role of asymmetry in producing a
triphasic rhythm while maintaining an observed dynamical invariant. Irregularity induced by ethanol [5] revealed the heterogeneity of
neuron activity within the CPG circuit, and the resultant irregular pattern could be explained by asymmetry of the synaptic connections.
Our recordings of CPG activity at irregular regimes illustrate that the
dynamics of neurons and their connections actively bound flexibility
to produce a coordinated robust rhythm.
The distinct sources of asymmetry in the model, in particular maximal
conductances and two different synaptic time scales, play a key role in
producing triphasic rhythms similar to the pyloric’s CPG. Overall, our
experimental and modeling results show that the study of asymmetric circuit components and their dynamical interaction help to understand how flexibility and robustness are balanced in central pattern
generator circuits.
Acknowledgements
We acknowledge support from MINECO/FEDER DPI2015-65833-P
and TIN2014-54580-R (http://www.mineco.gob.es/) and ONRG grant
N62909-14-1-N279.
References
1. Marder E, Calabrese RL: Principles of rhythmic motor pattern generation.
Physiol Rev 1996, 76:687–717.
2. Selverston AI, Rabinovich MI, Abarbanel HDI, Elson R, Szücs A, Pinto RD,
Huerta R, Varona P: Reliable circuits from irregular neurons: a dynamical
approach to understanding central pattern generators. J Physiol 2000,
94:357–374.
3. Elices I, Varona P: Closed-loop control of a minimal central pattern generator network. Neurocomputing 2015, 170:55–62.
4 Elices I, Varona P: Asymmetry Factors Shaping Regular and Irregular Bursting
Rhythms in Central Pattern Generators. Front Comput Neurosci 2017, 11:9.
5. Elices I, Arroyo D, Levi R, Rodriguez FB, Varona P: Assessing irregularity and
coordination of spiking-bursting rhythms in central pattern generators.
BMC Neuroscience 2016, 17(Suppl 1):O1
P283
Rivalry with irregular spiking: resolving mutual inhibition and the
balanced state
Ben Cohen, Carson Chow, Shashaank Vattikuti
Lab of Biological Modeling, NIDDK/NIH, Bethesda, MD, 20814, USA
Correspondence: Ben Cohen (benjapaulcohen@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P283
Perceptual rivalry is the subjective experience of alternations between
competing percepts when an individual is presented with an ambiguous stimulus. Rivalry has been modeled extensively, and was recently
proposed as a canonical neural computation [1]. Mutual inhibition, a
network architecture where pools of neurons inhibit one another, has
been the cornerstone of models of rivalry [2]. In such a network, inhibition dominates one pool, leading to sparse firing, while the opposing
pool fires rigorously under net excitation. This difference in inputs is
what drives rivalry, and yet it appears to conflict with balanced state
theory, in which net excitation and inhibition approximately balance.
Balanced state theory has been used to explain how neurons fire irregularly in response to stimuli, exhibiting Poisson-like inter-spike-interval
histograms [3]. Therefore, we investigated rivalry with asynchronous
irregular spiking in a ring of leaky integrate-and-fire neurons. We find
that rivalry can exist in synchronous or asynchronous, as well as regular or irregular states, and we delineate parameter regimes for each.
BMC Neurosci 2017, 18(Suppl 1):60
References
1. Shashaank Vattikuti, Phyllis Thangaraj, Hua W. Xie, Stephen J. Gotts, Alex
Martin, Carson C. Chow: Canonical Cortical Circuit Model Explains Rivalry,
Intermittent Rivalry, and Rivalry Memory. PLOS Computational Biology, 2016,
12(5):e1004903
2. Jeffery Seely, Carson C. Chow: Role of mutual inhibition in binocular rivalry.
J Neurophysiol, 2011, 106:2136 –2150
3. van Vreeswijk C, Sompolinsky H: Chaotic Balanced State in a Model of
Cortical Circuits. Neural Computation 1998, 10:1321–1371
P284
Interplay between inhibition and connectivity structure in driving
synchronization and functional properties of neural networks
Elena Bertolotti1,2, Raffaella Burioni1,2, Matteo di Volo3,4,5, Alessandro
Vezzani1,6
1
Department of Mathematical, Physical and Computer Sciences,
University of Parma, Parma, Italy, 43124; 2INFN, Gruppo Collegato di
Parma, Parma, Italy, 43124; 3Group for Neural Theory, Departément des
Etudes Cognitives, Ecole Normale Supérieure, Paris, France; 4Centro
Interdipartimentale per lo Studio delle Dinamiche Complesse, Sesto
Fiorentino, Italy, 1-50019; 5Indiana University–Purdue University,
Indianapolis, Indiana 46202, USA; 6IMEM-CNR, Parma, Italy, 43124
Correspondence: Elena Bertolotti (elena.bertolotti1@fis.unipr.it)
BMC Neuroscience 2017, 18 (Suppl 1):P284
We theoretically and numerically investigate the interplay between
the presence of a fraction of inhibitory neurons in a neural network
and their hub character (their relative connectivity with respect of the
rest of the network units) in synchronization and input processing of a
neural network. The starting point comes from a recent paper by Bonifazi et al. [1], which has put into evidence that hub neurons are typically inhibitory, suggesting a unifying view of cooperation between
inhibition and connectivity structure as a driving of synchronization
properties in neural networks. In our model, we consider a leaky-integrate-and-fire neural network composed by inhibitory and excitatory
neurons with a short term synaptic plasticity mechanism. In order to
emphasize the control role of highly connected neurons, both in input
and in output direction, we build networks where input and output
connectivities are the same for each neuron. Moreover, we apply a heterogeneous mean-field approach to the finite size network dynamics,
that lets us speed up numerical computations and highlight the role of
neuronal connections distributions. Then we can tune the fraction of
inhibitory neurons fI and their connectivity level to study the cooperation between hub character and inhibition.
We show how the interplay of these two ingredients gives rise to
a wide range of dynamical regimes and different ability to process
external inputs. Depending on fI, highly connected inhibitory nodes
strongly drive the synchronization properties of the overall network
through dynamical transitions from partially synchronous to asynchronous regimes. Furthermore, a metastable regime with long-time memory of external inputs emerges for a specific fraction of hub inhibitory
neurons, underlining the role of inhibition and connectivity also for
input processing in neural networks.
References
1. Bonifazi P, Goldin M, Picardo MA, Jorquera I, Cattani A, Bianconi G, Represa
A, Ben-Ari Y, Cossart R: GABAergic hub neurons orchestrate synchrony in
developing hippocampal networks. Science 2009, 326:1419–1424
P285
A mechanistic model of dopamine modulated learning in the
olfactory system of Drosophila
Bayar Menzat, Tim P. Vogels
Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
Correspondence: Bayar Menzat (bayar.menzat@cncb.ox.ac.uk)
BMC Neuroscience 2017, 18 (Suppl 1):P285
The fruit fly memorises previous experiences of different odours along
with information on whether the odour was associated with reward or
punishment in an area called the mushroom body. The pairing of an
odour with reward or shock process leads to odour specific modification
Page 161 of 176
in the responses of motor biasing output neurons (MBONs). MBONs
receive excitatory input from odour identity coding Kenyon Cells (KCs).
The connection between KCs and MBONs is targeted by dopaminergic
neurons (DAs) [1]. The KC-MBON synapses change their efficacies when
learning occurs, with experimental evidence proposing that the SpikeTiming-Dependent Plasticity synaptic change rule is present [2]. STDP
changes the strength of a connection between two neurons depending
on the precise timing of their action potentials [3]. When an odour is presented alone the firing rate of MBONs has been shown to increase, while
paired odour and reinforcement presentation has shown to decrease
the activity of the same MBONs [4, 5]. Here, we introduce a reward modulated STDP learning rule, where the learning rate of STDP is controlled
by the firing rate of dopaminergic neurons. To test this learning rule, we
simulate simultaneous presentation of odour and reward in a spiking
model of the olfactory circuit. We find that our learning rule can create
a stable memory of the value (positive, negative or neutral) of an odour
in the excitatory weights of the KC-MBON synapses. Our model can
reproduce experimentally observed bi-directional changes in the firing
rates of MBONs after learning. Finally, we show that when we combined
our learning rule with excitatory feedback from MBONs to DA neurons,
dopamine modulated learning plasticity can provide a mechanism for
learning the uncertainty of a reward. Building on this result, we make
experimental predictions of which odour will be selected between two
odours associated with uncertain reward. By proposing a simple mechanistic model of dopamine mediated learning our work has improved the
understanding of the role of dopamine in the fruit fly olfactory learning.
References
1. Scott Waddell, Neural Plasticity: Dopamine Tunes the Mushroom Body
Output Network, Current Biology 2016, 26, no. 3, R109–12, doi:10.1016/j.
cub.2015.12.023.
2. Stijn Cassenaer and Gilles Laurent: Conditional Modulation of SpikeTiming-Dependent Plasticity for Olfactory Learning. Nature 2012, 482, no.
7383: 47–52, doi:10.1038/nature10776.
3. Guo-qiang Bi and Mu-ming Poo: Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and
Postsynaptic Cell Type. Journal of Neuroscience 1998, 18, no. 24: 10464–72,
doi:10.1038/376074a0.
4. David Owald et al.: Activity of Defined Mushroom Body Output Neurons
Underlies Learned Olfactory Behavior in Drosophila, Neuron 2015, 86, no. 2:
417–27, doi:10.1016/j.neuron.2015.03.025.
5. Toshihide Hige et al.: Heterosynaptic Plasticity Underlies Aversive Olfactory
Learning in Drosophila. Neuron 2015, 88, no. 5: 985–98, doi:10.1016/j.
neuron.2015.11.003.
P286
Saliency‑based Gaze Prediction based on the Neural Population
for Integrating the Direction of Figure
Nobuhiko Wagatsuma
School of Science and Engineering, Tokyo Denki University, Hiki, Saitama
350-0394, Japan
Correspondence: Nobuhiko Wagatsuma (nwagatsuma@rd.dendai.ac.jp)
BMC Neuroscience 2017, 18 (Suppl 1):P286
Selective attention is a function of the brain that allocates its computational resource to the momentarily most important subsets of a visual
scene. Saliency models were used to predict the locations of selective attention and gaze [1]. I propose the biologically plausible saliency model based on the neural population for integrating activities
in intermediate-level visual areas with neurons selective to the direction of figure (DOF). Russell et al. demonstrated that the DOF integration played an important role for computing saliency [2]. In addition,
computational study hypothesized that a vast variety of surrounding
organizations by connections from early- to intermediate-level visual
areas were a basis for the neural selectivity of the DOF [3]. I extended
the previous saliency model [2] by introducing a variety of spatial patterns of synaptic connectivity for integrating the neural responses to
the DOF. In this work, a population of model neurons underlay the
determination of saliency magnitude. I tested hundreds of DOF organizations, and found that my proposed saliency model not only reproduced the characteristics of perceptual organization but also captured
BMC Neurosci 2017, 18(Suppl 1):60
object locations in natural images (Figure 1A). Furthermore, the gaze
prediction accuracy shown by my saliency mechanism was significantly higher than that by previous models [1, 2] (Figure 1B). These
results suggested a crucial role of various synaptic patterns in DOF
integration and a neural population coding of saliency to determine
selective attention and predict the locations of gaze.
Page 162 of 176
(glva) is indeed higher in OFF than ON cells, but our results (Figure 1)
suggested further that glva is the main contributor to the differences
between ON and OFF cells. In addition, we found that the voltagegated sodium channel conductance may be very different in ON and
OFF cells, as also suggested in [2]. In addition, our analysis (not shown
here) also suggests that mainly the fast inactivating sodium current,
not the persistent sodium current, play a significant role in generating distinct properties in ON and OFF GCs. Overall, our single neuron
model with optimised ion channel parameters was able to demonstrate a major dependence of ON and OFF cell specific intrinsic activity
on the sodium and calcium currents.
Table 1. Optimized intrinsic parameters in ON and OFF GCs (%
gap = 100*(((OFF value-ON value)/ON value))
Figure 1. Simulation results. A. Examples of images and saliency
maps calculated using previous models [1, 2] and my proposed
model. B. Results of gaze estimation. I drew receive operating characteristics (ROC) curves with MIT data set (1003 images and fixation data) [4] for quantifying the responses of saliency models
Acknowledgements
This work was partly supported by KAKENHI (no.26880019) and Technology of Tokyo Denki University Grant Number Q16 J-04.
References
Parameter
ON
OFF
% gap
Leak reversal potential (mV), VL
−60.4
−58.1
4
Leak (S/cm2), ḡL
4E−05
3E−05
20
A-type potassium (S/cm2), ḡK ,A
0.035
0.036
Low-voltage-activated calcium (S/cm2), 1E−04
ḡlva
5
8E−04
523
73
Persistent sodium (S/cm2),ḡNap
1E−07
3E−08
Calcium (S/cm2), ḡCa
0.008
0.009
11
Hyperpolarization-activated (S/cm2), ḡh
4E−06
3E−06
23
Potassium (S/cm2), ḡk
0.049
0.059
20
Ca-activated potassium (S/cm2), ḡk(Ca)
7E−05
9E−05
39
Fast inactivating sodium (S/cm2), ḡNa
0.025
0.077
207
1. Itti L, Koch C, Niebur E: A model of saliency-based visual attention for rapid
scene analysis. IEEE Trans Pattern Analy Mach Intell 1998, 20:1254–1259.
2. Russell AF, Mihalas S, von der Heydt R, Niebur E, Etienne-Cummings R: A
model of proto-object based saliency. Vision Res 2014, 94:1–15.
3. Sakai K, Nishimura H: Surrounding suppression and facilitation in the
dtermination of border ownership. J Cogn Neurosci 2006, 18: 562–579.
4. Judd T, Ehinger K, Durand F, Torralba A: Learning to predict where human
look. IEEE International Conference on Computer Vision 2009, 12: 2106–2113.
P287
Investigating the differences in ion channel properties
between ON and OFF ganglion cells: a combination of modelling
and optimization approach
Susmita Saha1, Reena Kapoor1, Robert Kerr2, John Wagner1
1
IBM Research Australia, VIC 3006, Melbourne, Australia;
2
NeuroEngineering Laboratory, Electrical & Electronic Engineering, The
University of Melbourne, Melbourne, Australia
Correspondence: Susmita Saha (susmitas@au1.ibm.com)
BMC Neuroscience 2017, 18 (Suppl 1):P287
Experimental studies have demonstrated differences in the intrinsic
physiological responses between ON and OFF retinal ganglion cells
(RGCs). OFF cells exhibit intrinsic spontaneous activity, subthreshold
membrane potential oscillations, rebound excitation and burst firing.
ON cells display none of the aforementioned intrinsic phenomena.
Previous modeling studies [1, 2] showed how special properties of
low-voltage-activated (T-type) calcium currents can explain the physiological differences between ON and OFF RGCs, while assuming that
most of the other ion channel properties are similar. In our study, using
a combination of computer simulations of single compartment, Hodgkin-Huxley type neurons and Bayesian optimisation, we optimised the
leak reversal potential and all literature-reported ion channel conductance densities against experimental findings from mouse ON and OFF
RGCs to estimate the potential contributions of other ion channels.
Optimising the larger set of conductances suggested two distinct sets
of parameters for ON and OFF cells (Table 1). In agreement with previous findings [1, 2], the low-voltage-activated calcium conductance
Figure 1. Relative % gap (round((%gap/total_gap) *100) between
two optimised parameter sets in ON and OFF RGC
Conclusion: The optimised cell-specific ion channel parameters imply
that ḡlva and ḡNa exhibited the most important role in explaining
intrinsic behavioural differences in ON and OFF retinal ganglion cells.
References
1. Kameneva T, Meffin H, Burkitt AN: Modelling intrinsic electrophysiological
properties of ON and OFF retinal ganglion cells. J. Comput. Neurosci 2011;
31:547–61.
2. Guo T, Tsai D, Morley JW, Suaning GJ, Kameneva T, Lovell NH, et al.: Electrical
activity of ON and OFF retinal ganglion cells: a modelling study. J. Neural
Eng 2016; 13:25005.
BMC Neurosci 2017, 18(Suppl 1):60
P288
Response reversal during top‑down modulation in cortical circuits
with multiple interneuron types
Luis C. Garcia del Molino1, Guangyu Robert Yang1, Jorge F. Mejias1,
and Xiao-Jing Wang1,2
1
Center for Neural Science, New York University, New York, NY 10003,
USA; 2NYU-ECNU Institute of Brain and Cognitive Science, NYU Shanghai,
Shanghai, 200122, China
Correspondence: Luis C. Garcia del Molino (garciadelmolino@nyu.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P288
Three major non-overlapping classes of interneurons expressing parvalbumin, somatostatin, or vasoactive intestinal peptide (henceforth
denoted PV, SST and VIP respectively) show cell type specific connectivity within themselves and with excitatory neurons leading to a
canonical microcircuit across cortex [1, 2]. Dissecting the dynamics of
this microcircuit is essential to our understanding of the mammalian
cortex. However, experiments recording from this circuit often report
counterintuitive and seemingly contradictory findings.
One particular example of complex behavior is the modulation of
responses to visual stimuli during locomotion, when V1 activity significantly increases with respect to immobility [3]. VIP interneurons
are known to be involved in such modulation because artificially activating (damaging) them mimics (blocks) the effect of running on visual response [4]. Since VIP cells inhibit SST cells which in turn inhibit
excitatory, PV and VIP cells, a natural explanation for this phenomenon is disinhibition [5]: upon activation of VIP cells the SST population
is inhibited and therefore neurons targeted by the SST population are
disinhibited, raising the overall rate of the excitatory neurons. However recent experiments show that the network behavior might be
more complex. In particular, in the absence of visual stimulation, the
activation of VIP cells results in an average decrease of SST population activity [4, 6] whereas in the presence of visual stimulation the
response of SST cells is reversed and its rate increases during locomotion [6, 7] which appears to challenge the disinhibition hypothesis.
This observation suggests that the nature of the interaction between
VIP and SST could be stimulus dependent.
We developed a general theoretical framework to explain such
response reversal, and we showed how these complex dynamics can
emerge in circuits that possess two key features: the presence of multiple interneuron populations and a non-linear dependence between
the input and output of the populations. Furthermore, we built a cortical circuit model and the comparison of our simulations with real data
shows that our model reproduces the complex dynamics observed
experimentally in mouse V1. Our explicit calculations allowed us to
pinpoint the connections critical to response reversal, and to predict
the existence of more types of complex dynamics that could be experimentally tested.
References
1. Jiang X, Shen S, Cadwell CR, Berens P, Sinz F, Ecker AS, Patel S, Tolias AS: Principles of connectivity among morphologically defined cell types in adult
neocortex, Science 2015, 350(6264):p.aac9462.
2. Pfeffer CK, Xue M, He M, Huang ZJ, Scanziani M: Inhibition of inhibition
in visual cortex: the logic of connections between molecularly distinct
interneurons. Nature Neuroscience 2013, 16(8):1068–1076.
3. Niell CM, Stryker MP: Modulation of Visual Responses by Behavioral State in
Mouse Visual Cortex. Neuron 2010, 65(4):472–479.
4. Fu Y, Tucciarone JM, Espinosa JS, Sheng N, Darcy DP, Nicoll RA, Huang ZJ,
Stryker MP: A Cortical Circuit for Gain Control by Behavioral State. Cell 2014,
156(6):1139–1152.
5. Wang XJ, Tegnér J, Constantinidis C, Goldman-Rakic PS: Division of labor
among distinct subtypes of inhibitory neurons in a cortical microcircuit of
working memory. PNAS 2004, 101(5):1368–1373.
6. Dipoppa M, Ranson A, Krumin M, Pachitariu M, Carandini M, Harris KD: Vision
and locomotion shape the interactions between neuron types in mouse
visual cortex. bioRxiv 2016, p.058396
7. Pakan JM, Lowe SC, Dylda E, Keemink SW, Currie SP, Coutts CA, Rochefort NL:
Behavioral-state modulation of inhibition is context-dependent and cell
type specific in mouse visual cortex. eLife. 2016, 23; 5:e14985.
Page 163 of 176
P289
Cellular and Network Modeling of the Striatal Microcircuit in a
Mouse Model of Huntington’s Disease
Hanbing Song1, Joseph Goodliffe2, Jennifer Luebke2, Christina M. Weaver1
1
Department of Mathematics, Franklin and Marshall College, Lancaster,
PA, 17604, USA; 2Department of Anatomy and Neurobiology, Boston
University School of Medicine, Boston, MA, 02118, USA
Correspondence: Hanbing Song (hsong1@fandm.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P289
Huntington’s disease (HD) is a neurodegenerative disorder of the central
nervous system characterized by movement, cognitive, and psychiatric
disturbance. In HD, massive structural and functional neuropathology
occurs in the striatum, particularly to spiny projection neurons (SPNs).
SPNs are part of either direct or indirect pathways of the basal ganglia,
and labeled either dSPNs or iSPNs respectively. The mechanisms underlying the vulnerability of SPNs in HD are unclear, and dSPNs and iSPNs
are functionally distinct and differentially affected in HD [1]. A commonly
employed transgenic mouse model of HD, Q175, exhibits changes of
molecular phenotypes, specific neuronal dysfunction, and subtle but
significant movement disorders [2]. Recent in vitro experiments on
12-month old animals showed that, compared to wildtype (WT), dSPNs
in Q175 animals showed increased input resistance, reduced rheobase
and reduced amplitude in action potentials. In addition, both dSPNs
and iSPNs exhibited greater dendritic complexity and lower spine density, along with altered frequencies of spontaneous post-synaptic currents (reduced excitatory and increased inhibitory). This project models
the SPNs of the striatum to further our understanding of these observed
changes in Q175 vs. WT mice. First, we used morphoelectrotonic transforms of reconstructed SPNs to predict that dendritic signal attenuation
is greater in SPNs from Q175 animals. Then, we used our parameter optimization method [3], implemented in NEURON (https://www.neuron.
yale.edu/neuron/) on a published 189-compartment conductancebased model SPN [4], to acquire a set of parameters depicting the passive membrane properties and active channel gating (conductance and
kinetics) of SPNs so that the difference of model output and empirical
recording experimental data could be minimized. We found proper fits
to the Q175 data only after increasing the branching complexity in the
published morphology. Differences in reversal potential of the leak channel and inward-rectifying potassium channel (KIR) contributed to the
increased excitability in Q175 dSPNs, consistent with empirical observations in mouse models of HD. Finally, we constructed a microcircuit
network model of both dSPNs and iSPNs and fast spiking interneurons
(FSIs) [5] that reflects the empirical findings and incorporates the optimized parameters in our single neuron study. In the WT network, dSPNs
and iSPNs fired in nearly equal proportion in response to input from FSIs
and the cortex and thalamus, predicting a balanced condition for motor
movements. Perturbing our model network consistent with the Q175
experiments (e.g., altering the frequency of synaptic inputs received by
SPNs) resulted in an imbalanced firing pattern among dSPNs and iSPNs,
consistent with what is thought to occur in HD pathology. These models provide a novel way to explore how individual neuron and network
properties contribute to functional pathology of the striatal microcircuit
in Q175 mice, in order to better understand the deleterious effects of
mutant Huntingtin in the human brain.
Acknowledgements
This research was supported by the CHDI Foundation, and used the
Neuroscience Gateway and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science
Foundation grant number ACI-1053575.
References
1. Galvan L, André VM, Wang EA, Cepeda C, Levine MS: Functional differences
between direct and indirect striatal output pathways in Huntington’s
disease. J Huntingtons Dis 2012, 1:17
2. Rangel-Barajas C and Rebec GV: Dysregulation of corticostriatal connectivity in Huntington’s disease: a role for dopamine modulation. J Huntingtons
Dis 2016, 5:303.
3. Rumbell TH, Draguljić D, Yadav A, Hof PR, Luebke JI, Weaver CM: Automated
evolutionary optimization of ion channel conductances and kinetics in
BMC Neurosci 2017, 18(Suppl 1):60
Page 164 of 176
models of young and aged rhesus monkey pyramidal neurons. J Comput
Neurosci 2016, 41:65
4. Evans RC, Morera-Herreras T, Cui Y, Du K, Sheehan T, Kotaleski JH, Venance
L, Blackwell KT: The effects of NMDA subunit composition on calcium influx
and spike timing-dependent plasticity in striatal medium spiny neurons.
PLoS Comput Biol 2012, 8:e1002493.
5. Damodaran S, Evans RC, Blackwell KT: Synchronicity of fast-spiking interneurons balances medium-spiny neurons. J Neurophysiol 2014, 111:836.
P290
Convolutional Neural Network‑based Interictal Epileptiform
Discharge Detection
John Thomas1, Nishant Sinha2,3, Nikhita Shaju1,4, Tomasz Maszczyk1, Jing
Jin1, Sydney S. Cash5, Justin Dauwels1†, M. Brandon Westover5†
1
School of Electrical and Electronic Engineering, Nanyang Technological
University, Singapore 639798, Singapore; 2Institute of Neuroscience,
Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne,
UK; 3School of Computing Science, Newcastle University, Newcastle
upon Tyne, UK; 4Department of Electrical and Electronics Engineering,
Vellore Institute of Technology, Vellore, Tamil Nadu, India; 5Neurology
Department, Massachusetts General Hospital and Harvard Medical
School, Boston, MA, USA
Correspondence: Justin Dauwels (jdauwels@ntu.edu.sg)
†
Contributed equally as co-senior authors
BMC Neuroscience 2017, 18 (Suppl 1):P290
Interictal epileptiform discharges (IEDs) are identified as one of the
distinctive biomarkers of epilepsy. The clinical gold standard for IED
detection is visual inspection by trained clinical neurophysiologists
(CNs). Therefore, the diagnosis becomes a heavily expert-centered
process. Automated or semi-automated IED detection systems could
overcome this current problem. Convolutional neural networks (CNNs)
are multilayer feed-forward deep neural networks that are widely
applied for classification and prediction. In this study, we develop
an efficient CNN-based IED detector and compare the performance
with the traditional support vector machine (SVM)-based IED detector. A similar study has been performed in [1], but here we study the
problem in greater depth and on a much larger dataset. We analyze
30-minute EEG recordings of 93 patients with epilepsy. The data was
recorded according to the standard 10-20 electrode placement system at Massachusetts General Hospital (MGH), Boston. IEDs were
independently annotated by two CNs. Each IED was extracted as a
500-millisecond waveform. A total of 18,164 IEDs were extracted. The
CNN was developed using Tensorflow-r0.12 [2] with a Tesla K40 GPU.
We developed the CNN model with five layers: an input layer, convolutional layer, pooling layer, fully connected layer, and output layer. Four
convolutional filters, each of size (1 × 4), were applied in the convolutional layer. The Rectified linear unit (ReLU) activation function was
applied with 100 neurons in the hidden layer. Training was performed
until 99.99% training accuracy was obtained. To prevent over-fitting,
weights to the output layer were dropped with a probability of 50%
in each training epoch. 5-fold cross-validation results are presented in
Table 1. For diagnostic purposes, CNs are most concerned with identifying the presence vs absence of IEDs in any given EEG recording, as
opposed to detecting all instances of IEDs. False positives pose a major
challenge, as there are typically many more background waveforms
than IEDs (1000:1 imbalance). The CNN provides high sensitivity at
very low false positive rates (see Figure 1), and thus is substantially less
prone to false positives compared to the SVM.
Table 1. Cross-validation results for CNN and SVM-based IED
detector systems
Performance indices CNN
SVM
Sensitivity
99.09%
32.18%
Specificity
93.22%
98.95%
BAC
96.15%
65.57%
AUC
0.966
0.829
Figure 1. The ROC plots of CNN and SVM-based IED detector systems
Conclusions: The CNN-based IED detector outshines the traditional
SVM-based system and methods proposed in the literature in terms of
sensitivity and specificity. Moreover, this study considers a much larger
data set than similar studies in the literature.
References
1. A. R. Johansen, J. Jin, T. Maszczyk, J. Dauwels, S. S. Cash, and M. B. Westover:
Epileptiform Spike Detection via Convolutional Neural Networks, Proc. IEEE
ICASSP 2016, pp. 754–758.
2. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado,
A. Davis, J. Dean, M. Devin et al.: TensorFlow: Large-Scale Machine Learning
on Heterogeneous Distributed Systems, 2015.
Software available from tensorflow.org.
P291
Effects of Hebbian learning on networks of Kuramoto phase
oscillators with time delay
Maryam Karimian1, Domenica Dibenedetto1, Michelle Moerel1,2, Peter De
Weerd1,2, Thomas Burwick4, Ronald L. Westra1,3
1
Maastricht Centre for Systems Biology, Maastricht University, 6229ER,
Maastricht, The Netherlands; 2Department of Cognitive Neuroscience,
Maastricht University, 6229ER, Maastricht, The Netherlands; 3Department
of Data Science and Knowledge Engineering, Maastricht University,
6211LH, Maastricht, The Netherlands; 4Frankfurt Institute for Advanced
Studies, Goethe University Frankfurt, 60438 Frankfurt am Main, Germany
Correspondence: Maryam Karimian (maryam.karimian@maastrichtuniversity.nl)
BMC Neuroscience 2017, 18 (Suppl 1):P291
Neuronal oscillations are crucial for various cognitive functions, including learning. Among neuronal populations, patterns of synchronization can drive connectivity changes, in turn modifying oscillations and
synchronization. To study changes in oscillation patterns with learning, we modeled brain processing using a directed random network
of phase-coupled oscillators interacting according to the Kuramoto
model [1]. We incorporated two extensions into the Kuramoto model:
spatial embedding through coupling delays, and synaptic plasticity according to a Hebbian learning formulation containing learning
associated parameters [2], i.e. learning rate (LR), which determines the
speed of learning, and learning enhancement (LE), which limits the
range of coupling weights. We investigated the structural and functional changes in the network with learning using graph theory and
synchronization evaluating tools, respectively. To study the structural
changes, we calculated the small-worldliness (SW) [3] of the network
throughout the simulated time. Our preliminary results show that with
learning the network is reweighted into a new structure with relatively
high levels of SW (Fig. 1A), but a fully connected pattern. To study the
functional changes, we measured the degree of synchronization for
each combination of learning parameters. We observed that specific
BMC Neurosci 2017, 18(Suppl 1):60
combinations of learning parameters led the network to show features of either single cluster synchronization, or split oscillators into
two anti-phase synchronized clusters. These two synchronization features are evaluated by measuring global order parameters R1 and R2
[2], respectively. The introduction of time delay affected the network
dynamics and structure changes especially in early stages of learning (Fig. 1A, C), but the global behavior observed (Fig. 1B, C) doesn’t
change. In summary, this enhanced Kuramoto model seems promising as this induces a SW network topology. However, as the network
obtained after learning is almost fully connected (unlike the efficiency
typical for brain networks), crucial next steps include the exploration
of biologically plausible ways to prune the network in order to increase
the wiring cost efficiency, prior to the application of the model to neuroscientific data.
Page 165 of 176
recordings from 55 healthy controls. Source-space parcel timecourses
were computed for the same brain regions as the model. Compared to
previous work using coupled Kuramoto phase oscillators [4], we find
the Wilson-Cowan model is even more sensitive to the network coupling strength because the amplitude of oscillations in neural activity
can vary. ISP successfully adjusts local inhibition to balance excitatory
activity across the network (Fig. 1A), reducing this sensitivity. As a
result, at intermediate delays the network exhibits metastable dynamics and amplitude envelope functional connectivity that is well correlated with experimental data over a wide range of global coupling
strengths (Fig. 1B-1D). Simple neural mass models are largely unable
to predict frequency-specific connectivity, and we have focused primarily on alpha connectivity here. Future work will investigate simultaneous prediction of the different patterns of connectivity seen in
experimental data across frequency bands.
Figure 1. A. Small-worldliness changes over time for three combinations of (LE, LR) in different states of synchronization. Differences
(R1 - R2) averaged over the last 100 time steps (0.1 s) of simulation
time (500 s) for cases of having (B) and not having (C) time delay
References
1. Juan A. Acebrón, L. L. Bonilla, Conrad J. Pérez Vicente, Félix Ritort, and Renato
Spigler: The Kuramoto model: A simple paradigm for synchronization
phenomena. Rev. Mod. Phys 2005. 77.
2. Niyogi, R. K. & English, L. Q.: Learning-rate-dependent clustering and selfdevelopment in a network of coupled phase oscillators. Phys. Rev. E 2009,
80.
3. Bassett, D. S. & Bullmore, E. D.: Small-World Brain Networks. Neuroscientist
2006, 12: 512–523.
P292
Biophysical modelling of resting brain states and inhibitory
synaptic plasticity
Romesh Abeysuriya1, Jonathan Hadida1,2, Stamatios Sotiropoulos2, Saad
Jbabdi2, Mark Woolrich1,2
1
Oxford Centre for Human Brain Activity, Oxford, United Kingdom;
2
Oxford Centre for Functional MRI of the Brain, Oxford, United Kingdom
Correspondence: Romesh Abeysuriya (romesh.abeysuriya@psych.ox.ac.
uk)
BMC Neuroscience 2017, 18 (Suppl 1):P292
Achieving realistic neural dynamics in biophysical models typically
requires extremely fine tuning of parameters, whereas in the real brain
dynamics are robust even to significant changes including sleep and
wake. One possible explanation is that robust dynamics are facilitated
by homeostatic mechanisms that are able to dynamically rebalance
brain networks. In this study, we use one such mechanism, inhibitory
synaptic plasticity (ISP), to achieve a local balance between excitation and inhibition, and investigate the effect this has on resting brain
states. We simulated neural activity in 68 cortical brain regions using
the relatively simple Wilson-Cowan neural mass model. Each brain
region consists of an excitatory and an inhibitory population of neurons. Long-range white matter connections link excitatory populations with distance-dependent propagation delays, while inhibitory
connections are purely local. Anatomical connectivity weights were
estimated using pre-processed diffusion MRI data from the Human
Connectome Project [1] using probabilistic tractography (40 subjects).
ISP was incorporated in each brain region as a dynamic change in the
local inhibitory connection depending on the difference between
excitatory activity and a preselected target level of activity [2, 3].
For comparison to experimental data, we used resting state MEG
Figure 1. A. Local inhibition balances long-range excitation. B.
Correlation between data and model alpha band functional connectivity (amplitude envelope correlation). Alpha band functional
connectivity profiles C. in MEG data D. in the model
References
1. Sotiropoulos SN, Jbabdi S, Xu J, Andersson JL, Moeller S, Auerbach EJ, et al.
Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage 2013, 80:125–143.
2. Vogels TP, Froemke RC, Doyon N, Gilson M, Haas JS, Liu R, et al. Inhibitory
synaptic plasticity: spike timing-dependence and putative network function. Front. Neural Circuits 2013, 7:119.
3. Hellyer PJ, Jachs B, Clopath C, Leech R. Local inhibitory plasticity tunes
macroscopic brain dynamics and allows the emergence of functional brain
networks. Neuroimage 2016, 124:85–95.
4. Cabral J, Luckhoo H, Woolrich M, Joensson M, Mohseni H, Baker A, et al.
Exploring mechanisms of spontaneous functional connectivity in MEG:
How delayed network interactions lead to structured amplitude envelopes
of band-pass filtered oscillations. Neuroimage 2014, 90:423–435.
P293
Temporal pattern recognition and control of animat foraging
by evolving small networks of adaptive exponential
integrate‑and‑fire neurons
Chama Bensmail1,2, Volker Steuber1, Borys Wrobel2,3
1
Centre for Computer Science and Informatics Research, University
of Hertfordshire, Hatfield, AL10 9AB, UK; 2Biology, Adam Mickiewicz
University, 61-712 Poznan, Poland; 3Systems Modelling IOPAN, 81-701
Sopot, Poland
Correspondence: Chama Bensmail (chamabens@evosys.org)
BMC Neuroscience 2017, 18 (Suppl 1):P293
Temporal pattern recognition is a common computational task that
can be performed by neural networks. The networks investigated in
BMC Neurosci 2017, 18(Suppl 1):60
this work are evolved with a biologically inspired artificial life platform
[1], and consist of up to five adaptive exponential integrate-and-fire
neurons with parameters producing tonic spiking with constant input
current [2]. The animat forages in a 2D open world where it receives
signals—we will describe them as 3-letter words, but the letters can
be also seen as flashes of light (with 3 different colors) or musical
notes (with 3 different frequencies). These words are emitted from two
sources: a target, which emits the word ABC, and a distractor, which
emits all the other 26 words consisting of a, b and c. The words and
letters never overlap (see Figure 1). Each word lasts for 25-ms, with
2-ms intervals of silence between letters, and 20-ms intervals between
words. When the animat touches an object (initially placed randomly),
the object disappears, and another object of the same type reappears
in another random position. The animat is equipped with 6 sensors; 2
per letter, which provide the input to the network (one input for the
difference of signal intensity on two sides of the animat, and the other
input for the average of signal intensity). The animat has 2 actuators,
whose activity is driven by the number of spikes produced by the
output neurons during the previous 120-ms. When the activity of one
actuator (say, left) is higher, the animat turns (here, right); when both
activities are equal, the animat moves straight, when both activities
equal 0, the animat stops.
We used a genetic algorithm to obtain several small networks which
discriminate efficiently the target pattern from all the other 3-letter words. Our results show that evolving in the presence of small
amounts of noise on the duration of the letters results in a more
efficient discriminator than evolution without such noise. The noisy
pattern had letters with 5-, 7-, and 9-ms duration, with the duration ordered randomly, the pattern without noise had all letters with
7-ms duration. Only the networks evolved with a noisy pattern were
robust to even noisier patterns (for example, they recognized patterns
in which letters had 1-, 7-, and 13-ms duration, with random order of
duration). Both the networks evolved with and without noise on the
length of the letters were robust also to the change in the actuator
forces and on silence intervals between words (up to 200-ms); number
of objects in the world (up to 2 objects of each type).
Page 166 of 176
McGovern Institute for Brain Research, Beijing Normal University, Beijing
100875, China
Correspondence: Si Wu (wusi@bnu.edu.cn)
†
Equal contribution
BMC Neuroscience 2017, 18 (Suppl 1):P294
Continuous attractor neural networks (CANNs) have been widely
used as a canonical model for neural information representation [1].
They have been successfully applied to describe the encoding of a
number of continuous features in neural systems, such as orientation, moving direction, head direction, and spatial location of objects
[1]. It remains unclear, however, how a neural system acquires such a
network structure in practice. Compared to the Hopfield network, the
key property of a CANN is that it holds a continuous family of stationary states, which form an (approximately) flat subspace in the network
states, rather than being isolated with each other with high-energy
barriers. Hopfield network is learned by Hebbian learning from statistically independent memory patterns. It has been suggested that
a CANN may be learned by Hebbian learning from correlated patterns, and in the ideal situation, from continuously morphed patterns.
The challenge for memorizing correlated patterns is that the classical
Hebb rule merges correlated patterns into a single attractor, corresponding to the pattern having the maximum overlap with others. To
overcome this difficulty, two methods which modifies the Hebb rule
were proposed. One considers the “popularity” of a neuron, i.e., the
involvement of a neuron in all memory patterns [2]. If a neuron is very
popular, then its contribution in Hebbian learning is decreased accordingly. By this, the network can store some correlated patterns, but
requires that within a pattern, neuronal activities are statistically independent, a condition hardly satisfied in reality. The other approach
considers the “novelty” of a newly presented memory pattern, measured by the Hamming distance between the new pattern and those
already stored in the network [3]. If a new pattern is novel, then the
pattern is learned by the Hebb rule; otherwise, the learning effect is
diminished accordingly. This method works well in certain cases, but
still has the shortcoming of that the learned result is rather sensitive
to the presenting order of patterns. In this study, we propose a new
method to learn a CANN from correlated patterns. The method applies
the Hebb rule only after correlated patterns are orthogonalized by the
Gram-Schmidt rule [4]. In effect, this method contains two operations,
pattern separation and novelty detection, and these two operations
appear to be biologically plausible and may happen in Dentate Gyrus
and CA1, respectively. We apply this method to memorize continuous
morphed patterns and learn a CANN successfully. The result is shown
in Figure 1.
Figure 1. The champion environment and its highly noisy input
vector (from left to right: ABC, aac, ABC, ABC, aba, cab). When
a target is hit; it becomes a black circle. The green boxes are the
actuator activities and the height of the bars corresponds to the
sensory activity
Acknowledgements
This work was supported by the Polish National Science Center (project EvoSN, UMO-2013/08/M/ST6/00922).
References
1. Wróbel, B. Evolution of spiking neural networks robust to noise and damage for control of simple animats. In: Proceedings of the 14th International
Conference on Parallel Problem Solving from Nature 2016.
2. Naud, Richard, et al. Firing patterns in the adaptive exponential integrateand-fire model. Biological cybernetics 2008; 19.4-5:335.
P294
Learning Continuous Attractor Neural Networks from Continuously
Morphed Patterns
Xiaolong Zhou1,2,†, Zilong Ji2,†, Xiao Liu2, Yan Xia2, and Si Wu2
1
School of Systems Science, Beijing Normal University, Beijing 100875,
China; 2State Key Laboratory of Cognitive Neuroscience & Learning, IDG/
Figure 1. Learning a CANN from continuously morphed patterns.
A. The morphed patterns. B. The learned network has an (approximately) flat subspace of low energy storing the morphed patterns,
a key property of a CANN. C. The learned neuronal connection
weights are translational invariant in space, a characteristic of a
CANN
References
1. Wu S, Wong KM, Fung CA, Mi Y, Zhang W: Continuous attractor neural networks: candidate of a canonical model for neural information representation. F1000Research 2016, 5.
2. Kropff E, TrevesA: Uninformative memories will prevail: the storage of
correlated representations and its consequences. HFSP journal 2007, 1(4),
249–262.
3. Blumenfeld B, Preminger S, Sagi D, Tsodyks M: Dynamics of memory representations in networks with novelty-facilitated synaptic plasticity. Neuron
2006, 52(2), 383–394.
BMC Neurosci 2017, 18(Suppl 1):60
4. Srivastava V, Sampath S, Parker DJ: Overcoming Catastrophic Interference
in Connectionist Networks Using Gram-Schmidt Orthogonalization. PloS
one 2014, 9(9), e105619.
P295
Learning Peri‑saccadic Receptive Field Remapping in Lateral
Intraparietal Area from Visual Experience
Xiao Wang, Mingsha Zhang, and Si Wu
State Key Laboratory of Cognitive Neuroscience & Learning, IDG/
McGovern Institute for Brain Research, Beijing Normal University, Beijing
100875, China
Correspondence: Xiao Wang (wxbnu@mail.bnu.edu.cn), Mingsha
Zhang (mingsha.zhang@bnu.edu.cn), Si Wu (wusi@bnu.edu.cn)
BMC Neuroscience 2017, 18 (Suppl 1):P295
Our eyes move constantly at a frequency of 3–5 times per second,
called saccade. A saccade induces sweeping of visual images on the
retina, yet we perceive the world to be stable. It has been suggested
that the brain achieves this visual stability via predictive remapping of
neuronal receptive field (RF), i.e., neurons respond to stimuli appearing in their future receptive fields (FRFs) before the eye actually moves
[1]. Recently, Wang et al. unveiled the detailed time course of neuronal
peri-saccadic remapping in the lateral intraparietal area (LIP) [2]. They
found that around a saccade, the neuronal RF expands along the saccadic trajectory temporally, covering the current RF (CRF), the FRF, and
the region the eye will sweep through during the saccade. A cortical
wave (CW) model was also proposed, which attributes RF remapping
as the consequence of neural activities propagated in LIP triggered
by the joint effect of visual stimuli and the corollary discharge (CD)
signal responsible for the saccade [2]. This CW model successfully
reproduced the experimental data, however, its biological plausibility remains unresolved. In this study, we address this issue by building
up a computational model to demonstrate that the CW model can be
naturally learned from visual experiences at the developmental stage
of the brain via the biologically plausible spiking-time-dependentplasticity (STDP).
We build a two-layer network, with one layer consisting of LIP neurons
and the other Superior Colliculus (SC) neurons. Initially, neuronal connections in LIP are bidirectional and weak, and the connections from
SC to LIP are non-selective. An eye movement due to saccade causes a
static visual image to “sweep” through the retina passively as if the visual
stimulus is moving in the opposite direction of the saccade. This “moving” stimulus activates LIP neurons sequentially in the retinotopic map;
meanwhile, SC neurons which convey the saccadic information also
respond due to the CD signal. Suppose this process repeats many times,
according to STDP, a connection path in the opposite direction of the
saccade between LIP neurons will be learned, and a connection from SC
to LIP matching the saccadic and remapping directions will be formed.
Over many such visual experiences at different spatial locations and in
different directions, a remapping network in LIP is completed. Consequently, a visual stimulus at FRF, combined with the CD signal from SC,
can elicit a cortical wave in LIP which propagates from FRF to CRF of the
neuron along the opposite direction of the saccade, exhibiting the perisaccadic RF remapping phenomenon as observed in the experiment.
References
1. Duhamel JR, Colby CL, Goldberg ME: The updating of the representation
of visual space in parietal cortex by intended eye movements [J]. Science
1992, 255(5040): 90.
2. Wang X, Fung CA, Guan S, Wu S, Goldberg ME, Zhang M: Perisaccadic
receptive field expansion in the lateral intraparietal area. Neuron 2016,
90(2):400–409.
P296
Axonal tree morphology dictates information coding
Netanel Ofer1,2, Orit Shefi1,2, Gur Yaari1
1
Faculty of Engineering, Bar Ilan University, Ramat Gan, 52900, Israel;
2
Institute for Nanotechnology and Advanced Materials, Bar Ilan University,
Ramat Gan 5290002, Israel
Correspondence: Netanel Ofer (netanel.ofer@biu.ac.il)
BMC Neuroscience 2017, 18 (Suppl 1):P296
Page 167 of 176
Neurons display various branching tree structures, and generate diverse
activity patterns [1–3]. Studying the influence of morphology on electrical activity is of crucial importance for understanding brain functionality [4]. We have investigated the connection between tree structure and
electrical activity by studying signal propagation and information coding
along two basic morphological building blocks: unbranched axonal segments, and axonal branching points. We did it by solving numerically the
Hodgkin Huxley model, as well as an adapted model for cortical neurons
[5], that enable usage of parameter values that reflect better the conditions for the mammalian nervous system. In an unbranched axon, spike
failures occur in high frequency trains. The effect on the propagated signal depends on the frequency of the spike train, axon diameter and axon
length. In axonal branching points, signals are modified in a way that
depends on the frequency of the spike train, and geometrical parameters of the branching point. Combined effects of these two elements can
lead to asymmetric responses even between two sibling branches with
identical diameters. These asymmetric conductions could be produced
from geometrical properties alone. We have systematically characterized
the firing patterns as a function of train frequency and morphometric
parameters, revealing distinct patterns of activities such as trains, blockage, intermitted trains, single or several spikes, and stuttering. Adding up
responses from many of these simple elements yields a rich repertoire of
non-trivial activities that can be used as encoding mechanism for computational tasks. In light of these new theoretical results, we have extended
our study to analyze real whole neuron structures, using reconstructed
data obtained from publically available large data repositories from NeuroMorpho.Org [6] and the Blue Brain Project [7, 8]. We have examined the
morphological parameters in all neuron types, and interneurons in particular. We clustered interneurons by their geometrical parameters and
divided them into groups according to the signal modulations that their
geometry dictates. Our results may advance interneurons classification by
axonal tree morphology, suggesting that different cells generate different
activity patterns. This detailed morphometric description of cells, together
with understanding how geometry determines information flow, opens
the door for deducing functionality from anatomical data.
References
1. DeFelipe J, López-Cruz PL, Benavides-Piccione R, Bielza C, Larrañaga P,
Anderson S, et al. New insights into the classification and nomenclature
of cortical GABAergic interneurons. Nature Reviews Neuroscience. 2013;
14:202–216.
2. Kepecs A, Fishell G. Interneuron cell types are fit to function. Nature. 2014;
505:318–326.
3. Polavaram S, Gillette TA, Parekh R, Ascoli GA. Statistical analysis and data
mining of digital reconstructions of dendritic morphologies. Frontiers in
neuroanatomy. 2014; 8:138.
4. Ofer N, Shefi O. Axonal geometry as a tool for modulating firing patterns.
Applied Mathematical Modelling. 2016; 40:3175–84.
5. Mainen ZF, Sejnowski TJ. Influence of dendritic structure on firing pattern
in model neocortical neurons. Nature. 1996;382:363.
6. Ascoli GA, Donohue DE, Halavi M. NeuroMorpho.Org: A Central Resource for
Neuronal Morphologies. J. Neurosci. 2007; 27:9247–51.
7. Markram H, Muller E, Ramaswamy S, Reimann MW, Abdellah M, Sanchez
CA, et al. Reconstruction and simulation of neocortical microcircuitry. Cell.
2015; 163:456–492.
8. Ramaswamy S, Courcol J-D, Abdellah M, Adaszewski SR, Antille N, Arsever
S, et al. The neocortical microcircuit collaboration portal: a resource for rat
somatosensory cortex. Frontiers in neural circuits [Internet]. 2015 [cited
2015 Dec 17];9. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/
PMC4597797/
P297
The Neuroscience Gateway Portal–High Performance Computing
for Neuroscientists
Ted Carnevale1, Amit Majumdar2, Subhashini Sivagnanam2, Kenneth
Yoshimoto2
1
Neuroscience Department, Yale University, New Haven, CT 06510, USA;
2
San Diego Supercomputer Center, University of California San Diego, San
Diego, CA 92093-0505, USA
Correspondence: Ted Carnevale (ted.carnevale@yale.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P297
BMC Neurosci 2017, 18(Suppl 1):60
Page 168 of 176
The Neuroscience Gateway Portal [1] (NSG http://www.nsgportal.org)
catalyzes computational neuroscience research that involves large
scale simulations and/or data analysis by lowering or eliminating
administrative and technical barriers to using High Performance Computing (HPC) resources. It does this by
(1) providing free and open access to supercomputers using time that
is acquired via the peer reviewed allocation process managed by the
Extreme Science and Engineering Discovery Environment (XSEDE),
the virtual organization that coordinates US academic supercomputer
centers
(2) offering a simple web-based user interface for accessing HPC
resources and
(3) more recently, adding a RESTful interface that enables programmatic access to HPC resources.
NSG is enabling participation by the wider neuroscience community
in research that would otherwise involve too great a computational
burden, such as large scale and detailed models of cells and networks, parameter optimization, brain image processing, connectome
pipelines etc. Since its inception in early 2013, it has provided about
10,000,000 core hours of supercomputer time to neuroscientists, and
has enabled more than 50 publications and posters. In addition, many
developers of new network modeling tools, data driven parameter
optimization pipelines (e.g. BluePyOpt from the Human Brain Project),
and data analysis tools are using the NSG to disseminate their results
to the neuroscience community.
NSG currently has about 450 registered users. Total core hour usage,
per-user core hour consumption rate, and the number of users have
all been growing at a rapid rate; annual usage is projected to exceed
10,000,000 core hours in 2017.
Developing and operating the NSG has given us a unique opportunity
to understand and analyze how a very diverse range of neuroscientists are using an environment like the NSG, and examine their growing need for supercomputer power, as well as associated issues and
needs for collaboration, data sharing/management and various forms
of computing.
clarify whether Rin change is caused by the disturbances of synaptic,
passive or active channels.
First, we compared electrophysiological properties before and after
the blockade of synaptic currents by APV, DNQX and bicuculline. Analysis showed that all of the parameters changed by SE sustain after the
blockade of synaptic currents. Thus, the main source of SE-induced
changes is not synaptic.
Next, we used the dynamic-clamp which allowed us to simulate
additional potassium and nonspecific currents [2]. We attempted to
clarify which of the neuronal properties are affected by the leak current (IL) and which ones are by the potassium current which induces
spike adaptation (Iad). The effect of IL in control rats was similar to the
effect of SE. Addition of the leak current led to statistically significant
decreases of Rin, τm, Imax, IF1, RA, magnitude of sag, and increases Rb,
stationary IF, and gain (only in ERC). We then mimicked Iad by using the
approximation from [3]. Addition of Iad did not change Rin, increases
IF1, and decreases firing rate, as well as time to the first spike.
Thus, the decrease of Rin after SE is more likely to be induced by IL as
well as the decrease of τm, time to the first spike, RA, IF1 and increase
of Rb. Overall, our results suggest that LP-induced SE mainly increases
the leak conductance and keeps other factors intact.
Acknowledgements
Supported by NSF 1458840 (A.M., S.S., K.Y); NSF 1458495, NIH/NIDCD
DC 009977, NIH/NINDS NS 011613 (T.C).
Reference
P299
Depolarizing GABA leads to interneuron‑based interictal
discharges: experimental and mathematical models
Anton V. Chizhov1,2, Dmitry V. Amakhin2, Aleksey V. Zaitsev2
1
Ioffe Institute, St.-Petersburg, 194021, Russia; 2Sechenov Institute
of Evolutionary Physiology and Biochemistry of RAS, St.-Petersburg,
194223, Russia
Correspondence: Anton V. Chizhov (anton.chizhov@mail.ioffe.ru)
BMC Neuroscience 2017, 18 (Suppl 1):P299
1. Sivagnanam S, Majumdar A, Yoshimoto K, Astakhov V, Bandrowski A,
Martone ME, Carnevale NT: Introducing the Neuroscience Gateway, Proceedings of the 5th International Workshop on Science Gateways, volume 993,
CEUR-WS.org, 2013. [http://ceur-ws.org/Vol-993/paper10.pdf ]
P298
Changes of electrophysiological neuronal properties
in lithium‑pilocarpine model of epilepsy
Elena Y. Smirnova1,2, Dmitry V. Amakhin2, Sergey L. Malkin2, Anton V.
Chizhov1,2, Aleksey V. Zaitsev2
1
Ioffe Institute, St.-Petersburg, 194021, Russia; 2Sechenov Institute
of Evolutionary Physiology and Biochemistry of RAS, St.-Petersburg,
194223, Russia
Correspondence: Elena Y. Smirnova (elena.smirnova@mail.ioffe.ru)
BMC Neuroscience 2017, 18 (Suppl 1):P298
The lithium-pilocarpine (LP) model of epilepsy belongs to a group
of animal models that replicate the general progression of events as
observed in humans [1]. We aimed to determine, whether LP-induced
status epilepticus (SE) affects electrophysiological properties of neurons and then, if does, what is the nature of the effect. Using wholecell patch-clamp we compared passive properties, single-spike and
spike-pattern attributes of pyramidal neurons in entorhinal cortex (ERC) slices of control (53 cells) and LP-treated rats (47 LP-cells,
recorded in a day after SE), as well as in, presumably, less damaged
prefrontal cortex (37 control vs 35 LP-neurons). LP-cells had reduced
input resistance (Rin), time constant (τm), first instantaneous frequency
(IF1) and amplitude of spikes (RA), and increased rheobase (Rb) and
current inducing maximal firing rate (Imax). Apart from the decrease
of spike amplitude, all the effects were stronger in ERC. Because the
decrease of Rin can explain all the other effects, we then aimed to
Acknowledgements
This work was supported by the Russian Science Foundation (project
16-15-10202).
References
1. Furman M: Seizure Initiation and Propagation in the Pilocarpine Rat Model
of Temporal Lobe Epilepsy. J Neurosci 2013, 33(42):16409 –16411.
2. Smirnova EY, Zaitsev AV, Kim KK, Chizhov AV: The domain of neuronal firing
on a plane of input current and conductance. J Comput Neurosci 2015,
39(2): 217–233.
3. Kopell N, Ermentrout GB, Whittington MA, Traub RD: Gamma rhythms and
beta rhythms have different synchronization properties. Proc Nat Acad Sci
USA 15 2000, 97(4):1867–1872.
In in vitro experimental model of temporal lobe epilepsy, we observe
the repeating sequences of interictal discharge (IID) regimes and seizure-like events, where IID are initiated by interneurons. We used an
extracellular medium with high potassium/low magnesium concentration with the addition of 4-AP in order to provoke epileptiform activity in combined hippocampus/entorhinal cortex slices of the rat brain
[1]. Two types of IID were observed. For each type, AMPA, NMDA, and
GABA-A synaptic components have been estimated by means of multiple recordings on different voltage levels in the voltage-clamp whole
cell configuration. As found, IIDs of the first type (IID1) reflect synchronization in the network of interneurons, thus they are characterized by
a pure GABAergic current recorded in an excitatory neuron. IIDs of the
second type (IID2) are composed of early GABAergic and later glutamatergic components.
We have reproduced the IIDs in our mathematical model, using the
conductance-based refractory density approach [2] which provides
both a biophysically detailed description of neuronal populations in
terms of ionic channel conductances for one- or two-compartment
neurons and good precision for statistically equilibrium and nonequilibrium regimes of ensemble activity. Coupled excitatory and
inhibitory neurons interact via glutamatergic and GABAergic plastic synapses. IID1 s and IID2 s were well reproduced in the model. In
simulations, the only parameter that controlled the regimes was the
reversal potential of GABA-A current, VGABA. Switching from the control silent state to IID1 s and then to intermittent IID2 s and IID1 s, and
BMC Neurosci 2017, 18(Suppl 1):60
finally, only IID2 s occurs with depolarization of VGABA. We hypothesize
that in the experiments VGABA was depolarized because of depressed
action of potassium-chloride cotransporters in the conditions with
high extracellular potassium concentration. We have also found the
synaptic depression to be a crucial factor, which provides ceasing of
each of the discharges and determines their duration. Overall, our
study reveals the mechanisms of pathological synchronization with
the primary role of excitatory GABA receptors in the interneuronal
network.
Acknowledgements
This work was supported by the Russian Science Foundation (project
16-15-10201).
References
1. Amakhin DV, Ergina JL, Chizhov AV, Zaitsev AV: Synaptic Conductances
during Interictal Discharges in Pyramidal Neurons of Rat Entorhinal Cortex.
Front. in Cell. Neuroscience 2016, 10: 233.
2. Chizhov AV, Graham LJ: Population model of hippocampal pyramidal neurons, linking a refractory density approach to conductance-based neurons.
Phys Rev E 2007, 75: 011924.
P300
Neural activity in distinct navigation modes of flying pigeons
Margarita Zaleshina, Alexander Zaleshin
Moscow Institute of Physics and Technology, Moscow, 117303, Russia
Correspondence: Margarita Zaleshina (zaleshina@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P300
In spatial navigation, there are certain tasks of choosing a route or
correcting a route depending on external conditions. It is necessary
to respond to changes in the visual environment, to compare the
expected and observed landscape. Quick reaction is required in case
of unexpected obstacles on the pathway. Perception is detailed as the
target is approached. Orientation awareness is accompanied by various types of neuronal activities. It can be observed in brain cells associated with navigation (including place cells, grid cells, head-direction
cells) [1], and in combinations of rhythms of neural ensembles [2].
Contributions of different band oscillations during route selection
can be independent [3]. Observed brain activities are different in tasks
with the specified position of the visual cue or with underspecified
movement goal [4].
This work proposes a model to identify the characteristics of brain
activity of flying pigeons with different modes of space perception.
Pigeons fly home based on familiar landmarks and landscape features [5], solar, stellar and magnetic cues, polarized light patterns [6],
and other references to geographical location. Pigeons have color and
ultraviolet vision, their eyes distinguish the 75 frames per second, field
of view is 340 degrees. Comparison of EEG responses to visual landmarks in flying pigeons was described [7].
The work considers pigeon flight on known route in three modes: 1.
Stationary flight at an altitude of 100-300 meters, speed of 60 km/h.
For flight in a given direction it is necessary to take into account the
influence of wind (drift angle). 2. Response to danger or sudden
changes. Pigeons are more sensitive to radial motion when there is an
acceleration as opposed to a constant velocity [8]. 3. Descent
and
landing. Birds begin to fly in circles at an altitude of 30-50 meters.
In the computational model, it is assumed that each mode is accompanied by a characteristic set of rhythms of neural ensembles (for quiet
flight, for alarm and for approaching to visible goal). Representation
of brain activity as sets of rhythms depends on the type of mode. In
model, recognition of textures and borders in the mode “stationary
flight” is additionally encoded by the phase of rhythms with lower frequency. Interactions between cortical rhythms may generate a third
frequency [9]. Route reference points are additionally encoded by the
amplitude of rhythms in all modes. QGIS (http://www.qgis.org) allows
to integrate data received from various sources simultaneously. In the
work, GPS track of flights and landscape maps are performed in QGIS
(similarly, QGIS was applied in [6]). In addition, the program allows to
combine results of EEG data processing with the spatial characteristics
Page 169 of 176
of pigeon flight. In the spatial representation of the model takes into
account the distances between the reference points on the ground.
References
1. Spiers HJ, Barry C: Neural systems supporting navigation. Curr Opin Behav Sci
Elsevier Ltd 2015, 1:47–55.
2. Watrous AJ, Fell J, Ekstrom AD, Axmacher N: More than spikes: Common
oscillatory mechanisms for content specific neural representations during
perception and memory. Curr Opin Neurobiol Elsevier Ltd 2015, 31:33–39.
3. Brinkman L, Stolk A, Marshall TR, Esterer S, Sharp P, Dijkerman HC, et al.:
Independent Causal Contributions of Alpha- and Beta-Band Oscillations
during Movement Selection. J Neurosci 2016, 36:8726–8733.
4. Gertz H, Lingnau A, Fiehler K: Decoding Movement Goals from the FrontoParietal Reach Network. Front Hum Neurosci 2017, 11:84.
5. Gagliardo A, Ioale P, Savini M, Dell’Omo G, Bingman VP: Hippocampaldependent familiar area map supports corrective re-orientation following
navigational error during pigeon homing: A GPS-tracking study. Eur J
Neurosci 2009, 29:2389–2400.
6. Blaser N, Guskov SI, Meskenaite V, Kanevskyi VA, Lipp HP: Altered Orientation
and Flight Paths of Pigeons Reared on Gravity Anomalies: A GPS Tracking
Study. PLoS One 2013, 8(10):e77102.
7. Vyssotski AL, Dell’Omo G, Dell’Ariccia G, Abramchuk AN, Serkov AN, Latanov
AV., et al.: EEG Responses to Visual Landmarks in Flying Pigeons. Curr Biol
Elsevier Ltd 2009, 19:1159–1166.
8. Nankoo JF, Madan CR, Spetch ML: Wylie DR: Perception of complex
motion in humans and pigeons (Columba livia). Exp Brain Res 2014,
232:1843–1853.
9. Roopun AK: Temporal interactions between cortical rhythms. Front Neurosci
2008, 2:145–154.
P301
The Role of the Receptive Field Structure in Neuronal Compressive
Sensing Signal Processing
Victor J. Barranca, George Zhu
Department of Mathematics and Statistics, Swarthmore College,
Swarthmore, PA 19081, USA
Correspondence: Victor J. Barranca (vbarran1@swarthmore.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P301
The receptive field structure ubiquitous in the visual system is believed
to play a crucial role in encoding stimulus characteristics, such as
contrast and spectral composition. However, receptive field architecture may also result in unforeseen difficulties in processing particular
classes of images. We explore the potential functional benefits and
shortcomings of localization and center-surround paradigms in the
context of an integrate-and-fire neuronal network model. Utilizing
the sparsity of natural scenes, we derive a compressive-sensing based
theoretical framework for network input reconstructions based on
neuronal firing rate dynamics [1, 2]. This formalism underlines a potential mechanism for efficiently transmitting sparse stimulus information, and further suggests sensory pathways may have evolved to take
advantage of the sparsity of visual stimuli [3, 4]. Using this methodology, we investigate how the accuracy of image encoding depends on
the network architecture.
We demonstrate that the receptive field structure does indeed facilitate marked improvements in natural stimulus encoding at the price
of yielding erroneous information about specific classes of stimuli. Relative to uniformly random sampling, we show that localized random
sampling yields robust improvements in image reconstructions, which
are most pronounced for natural stimuli containing a relatively large
spread of dominant low frequency components. This suggests a novel
direction for compressive sensing theory and sampling methodology
in engineered devices. However, for images with specific gray-scale
patterning, such as the Hermann grid depicted in Fig. 1, we show that
localization in sampling produces systematic errors in image encoding that may underlie several optical illusions. We expect that these
connections between input characteristics, network topology, and
neuronal dynamics will give new insights into the structure-function
relationship of the visual system.
BMC Neurosci 2017, 18(Suppl 1):60
Figure 1. A. The network-averaged firing rate dependence on the
external-drive strength scaling, computed using model simulation
and theoretical linear input-output mapping. B. Original image
(left) and CS reconstruction (right) using localized random sampling of the network dynamics. C. Hermann grid illusion. D. Reconstructions of (C) for various choices of receptive field size scaling
and excitatory center region radius
References
1. Field DJ: What is the Goal of Sensory Coding? Neural Comput. 1994, 6:
559–601.
2. Candes EJ, Romberg JK, Tao T: Stable Signal Recovery from Incomplete
and Inaccurate Measurements. Commun Pur Appl Math. 2006, 59(8):
1207–1223.
3. Barranca VJ, Kovacic G, Zhou D, Cai D: Sparsity and Compressed Coding in
Sensory Systems. PLoS Comp Biol. 2014, 10(8):e1003793.
4. Barranca VJ, Kovacic G, Zhou D, Cai D: Improved Compressive Sensing of
Natural Scenes using Localized Random Sampling. Sci Rep. 2016, 6:31976.
P302
FuNS with E/I balance: critical dynamics maximize stability of neural
networks
Quinton M. Skilling1, Daniel Maruyama2, Nicolette Ognjanovski3, Sara J.
Aton3, and Michal Zochowski1,2
1
Biophysics Program, University of Michigan, Ann Arbor, MI 48109,
United States; 2Department of Physics, University of Michigan, Ann
Arbor, MI 48109, United States; 3Department of Cellular, Molecular,
and Developmental Biology, Ann Arbor, MI 48109, United States
Correspondence: Quinton M. Skilling (qmskill@umich.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P302
The mammalian brain naturally balances excitation and inhibition.
This results in complex dynamics vital for important cognitive functions such as the formation of new memories. Excitatory/inhibitory
(E/I) balance has been shown to result in scale-free distributions
of population behavior known as neuronal avalanches, a hallmark
of self-organized criticality in the brain. Recently, we have shown
using models well-rooted in physics that new memories are stored
only when the system dynamics reside near a critical point and are
characterized by enhanced stability of spiking activity which we
refer to as functional network stability (FuNS) [1]. Here, we expand
on this work through direct modeling of neuronal networks where
E/I balance is tightly controlled. Proximity to criticality at E/I balance is verified via calculation of neuronal avalanches as well as
through calculating functional connectivity correlation between
neurons for increasing separation distance between them. Introducing a region of increased coupling, such as the synaptic potentiation involved in learning, increases FuNS in networks exhibiting
E/I balance significantly over networks whose dynamics arise primarily through excitatory or inhibitory inputs. Our results indicate
that networks with balanced excitation and inhibition have an
increased ability to store memories through increased functional
network stability, a phenomenon due in part to critical dynamics in
neural systems.
Reference
1. Skilling QM et al. 24 Feb 2017. “Criticality, stability, competition, and consolidation of new representations in brain networks.” arXiv: 1702.07649.
Page 170 of 176
P303
Neural oscillations modulate the network dynamics around E‑I
balance in memory consolidation
Jiaxing Wu1, Nicolette Ognjanovski2, Sara Aton2, Michal Zochowski3
1
Applied Physics Program, University of Michigan, Ann Arbor, MI, 48109,
USA; 2Department of Molecular, Cellular, and Developmental Biology,
University of Michigan, Ann Arbor, MI, 48109, USA; 3Biophysics Program
and Department of Physics, University of Michigan, Ann Arbor, MI, 48109,
USA
Correspondence: Jiaxing Wu (jxwu@umich.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P303
Rhythmic activities of different frequency bands have been observed
universally in the brain and are thought to play important roles in
various cognitive processes [1]. However, the fundamental mechanism of how these neural oscillations contribute to brain activities is
still an open question. Recently, via both computational simulations
and in vivo experiments, we found that oscillations are essential for
memory consolidation as they mediate network functional stability. We have shown computationally, that various network properties
such as firing rate, synchrony, mean phase coherence are enhanced
in the presence of external oscillations around Excitatory-Inhibitory
(E-I) balance, where E-I ratio is calculated based on the excitatory and
inhibitory synaptic strength and neuronal firing frequency. We have
investigated this effect for both type 1 (integrator) neurons as well as
type 2 (resonator) cells. The networks composed of resonator neurons
are more sensitive to the oscillatory drive than the networks composed
of integrator neurons, however both show significant changes in firing
patterns. We show that global oscillations causally organize firing patterns between heterogeneous networks composed of dense neuronal
clusters that are loosely connected with each other, facilitating communication and information transfer between spatially distributed
brain regions. Most importantly, near Excitatory-Inhibitory (E-I) balance,
oscillations increase both functional connectivity between neurons
and coherence between spikes and local field potential (LFP), as well as
enhance network functional stability, thus leading to faster changes in
network structural connectivity patterns thought to underlie learning
and memory consolidation. These in silico observations are supported
by our experimental data [2]. In summary, our results show that neural
oscillations together with network state near E/I balance coordinate the
network dynamics and contribute to memory consolidation.
References
1. Buzsaki G: Rhythms of the Brain, Oxford University Press 2006.
2. Ognjanovski N, Schaeer S, Wu J, Mofakham S, Maruyama D, Zochowski M,
Aton SJ: Parvalbumin-expressing interneurons coordinate hippocampal
network dynamics required for memory consolidation. Nature Communications (In Press).
P304
Cellular and network properties of interneuron networks dictate
variable clustering patterns in both strictly inhibitory and E‑I neural
networks
Scott Rich1, Victoria Booth2, Michal Zochowski3
1
Applied and Interdisciplinary Mathematics Program, University
of Michigan, Ann Arbor, MI 48104, USA; 2Departments of Anesthesiology
and Mathematics, University of Michigan, Ann Arbor, MI 48104, USA;
3
Departments of Physics and Biophysics, University of Michigan, Ann
Arbor, MI 48104, USA
Correspondence: Scott Rich (sbrich@umich.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P304
The diverse population of interneurons in the hippocampus is pivotal to
the formation of oscillatory electrical activity that contributes to memory
processing [1], while in the cortex such interneurons and rhythms are
implicated in potential mechanisms underlying selective attention [2].
Computational research has shown that these rhythms can be generated
in purely inhibitory networks or networks with both excitatory and inhibitory neurons (E-I networks). However, the dynamics and mechanisms
generating them depend on properties of the inhibitory network.
BMC Neurosci 2017, 18(Suppl 1):60
Simulations of strictly inhibitory networks in our recently published
work [3] demonstrate that the intrinsic cellular properties and connectivity density alter the bursting properties exhibited in randomly connected, heterogeneous inhibitory networks. We analyze networks with
three types of model neurons that are classified into the classical Type
I or Type II categories using their current-frequency relation (IF curve)
and Phase Response Curve (PRC), while additionally investigating the
role of an M-type adaptation current adaptation current. Across simulations we vary the degree of cellular heterogeneity, the intrinsic firing
frequency of neurons, and the time scale of decay of synaptic inhibition. The observed dynamics often differ from those predicted by the
Interneuron Network Gamma (ING) mechanism [4], as well as from
results in all-to-all connected networks. While the networks studied
here synchronize into a single cluster of active neurons when said neurons are Type I, analogous networks of Type II neurons without adaptation segregate into two mutually exclusive clusters organized by the
cells’ intrinsic firing frequencies. When the neurons are modeled as
Type II with adaptation, we observe dynamics similar to those seen in
networks of either Type I or Type II neurons depending upon network
parameters, although the adaptation current does imbue these networks with additional unique behaviors. The mechanisms underlying
this variety of dynamics is explained by changes in profiles of the PRCs
across the different neuron types.
One additional property of Type I inhibitory networks is the different
synchrony patterns exhibited when the inhibitory synapses are strong
or weak. By expanding our research to E-I networks, we have shown
that this property plays an important role in the dynamics of excitatory
neurons in these larger networks. When inhibitory to inhibitory synapses (I-I) in an E-I network are sufficiently strong, the dynamics match
those predicted by the PING mechanism [5]. When these synapses are
weakened, networks exhibit rhythmic bursting for weaker excitatory
to inhibitory connectivity and can exhibit rhythms slower than the
typical gamma frequency, two features that expand upon the types of
dynamics typically described by PING theory. However, with weak I-I
synapses, the dynamics of the excitatory cell bursts tend to become
disorganized and aperiodic for stronger excitatory to inhibitory connectivity, due to more variable activity patterns in the inhibitory network. These results indicate that the strength of I-I connectivity plays a
crucial role in dictating the type, strength and robustness of excitatory
bursting patterns in an E-I network, analogously to how cell type dictates the type of dynamics seen in strictly inhibitory networks.
Page 171 of 176
for the brain to function in a coherent way. To exhibit these features,
brain networks are considered to have small-world and scale-free network structure properties [1].
Neurons in the brain communicate with each other via connections
called synapses in order to be able to synchronize and perform certain
tasks. Thus, failures at these synaptic connections have detrimental
effects such as loss of consciousness or neurodegenerative diseases.
Our objective is understanding the effect of decreased synaptic transmission on brain networks as a whole as well as on the formation of
globally coherent states. The results may be applied to understand
how anesthetics brings the loss of consciousness by changing brain
dynamics and early diagnosis of some neurodegenerative diseases
such as Alzheimer’s and ALS, in which cases synaptic failure is the earliest symptom [2, 3]. For this purpose, both small-world and scale-free
networks, which are prevalent in brain, are modeled with integrateand-fire excitatory neurons. Synaptic failure is introduced to the model
by a parameter which randomly determines whether neurons get signals from the others they’re connected to or not. This parameter also
depends on the spiking history of the neurons, i.e. synapses are more
likely to fail if the presynaptic neuron is more recently fired.
After various simulations, quantitative measures are done with neurons’ spike times in order to determine how synchronous and coherent
the networks behave. As a result, we demonstrate that more local connections favor more coherent and synchronous behavior with increasing synaptic transmission. Moreover, we show that scale-free networks
with different directionalities respond to synaptic failure in different
ways. However, neurons with moderate degrees are more coherent
than other neurons in all scale-free network structures. Also, when
hubs in scale-free networks are disconnected, the effect is bigger than
the disconnection of lower-degree neurons on the network. We also
observed that the dependence on spiking history affects synchronization and coherent state formation in different ways for different network structures.
References
1. Bocaletti S, Latora V, Moreno Y, Chavez M, Hwang D: Complex networks:
Structure and dynamics. Physics Reports. 2006;424(4-5):175–308.
2. Wishart T, Parson S, Gillingwater T: Synaptic Vulnerability in Neurodegenerative Disease. Journal of Neuropathology & Experimental Neurology.
2006;65(8):733–739.
3. Alkire M, Hudetz A, Tononi G: Consciousness and Anesthesia. Science.
2008;322(5903):876–880.
References
1. Bartos M, Vida I, Jonas P: Synaptic mechanisms of synchronized gamma
oscillations in inhibitory interneuron networks. Nat. Rev. Neurosci. 2007, 8:
45–56.
2. Fries P: A mechanism for cognitive dynamics: neuronal communication
through neuronal coherence. Trends Cogn. Sci. 2005, 9: 474–480.
3. Rich S, Booth V, Zochowski M: Intrinsic cellular properties and connectivity density determine variable clustering patterns in randomly connected
inhibitory neural networks. Front. Neural Circuits 2016, 10.
4. Whittington M, Traub RD, Kopell N, Ermentrout B, Buhl E. Inhibition-based
rhythms: experimental and mathematical observations on network
dynamics. Int. J. Psychophysiol. 2000: 38, 315–336.
5. Kopell N, Borgers C, Pervouchine D, Malerba P, Tort A. Gamma and theta
rhythms in biophysical models of hippocampal circuits in Microcircuits, A
Computational Modeler’s Resource Book, Springer Series in Computational
Neuroscience. 2010: 423–457.
P305
Synaptic failure in functional network activity
Maral Budak1, Michal Zochowski1,2
1
Department of Biophysics, University of Michigan, Ann Arbor, Michigan,
48109, USA; 2Department of Physics, University of Michigan, Ann Arbor,
Michigan, 48109, USA
Correspondence: Maral Budak (mbudak@umich.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P305
Human brain is a complex network including 1011 neurons connected
by 1014 synapses. Information processing occurs in separate, functionally specialized regions, which also coordinate and integrate efficiently
P306
Data‑driven multiscale model of mouse M1
microcircuits
Salvador Dura-Bernal1, Samuel A. Neymotin1,3, Benjamin A. Suter4, Gordon
M. G. Shepherd2, William W. Lytton1,5
1
Department Physiology & Pharmacology, SUNY Downstate, Brooklyn, NY
11203, USA; 2Department Physiology, Northwestern University, Chicago,
IL 60611, USA; 3Department Neuroscience, Brown University, Providence,
RI 02912, USA; 4Institute of Science and Technology (IST) Austria,
Klosterneburg, 3400, Austria; 5Kings County Hospital Center, Brooklyn, NY
11203, USA
Correspondence: Salvador Dura-Bernal (salvadordura@gmail.com)
BMC Neuroscience 2017, 18 (Suppl 1):P306
We developed a detailed multiscale computational model of mouse
primary motor cortex (M1) microcircuits, based on novel data provided by experimentalist collaborators. The model simulates at scale
a cylindrical volume with a diameter of 300 μm and cortical depth
1350 μm of M1. It includes over 10,000 cells distributed across cortical layers based on measured cell densities, with more than 40 million
synaptic connections. Neuron models were optimized to reproduce
the current-clamp electrophysiological properties of major classes of
M1 neurons, with a special emphasis on layer 5 corticospinal (SPI) and
corticostriatal (STR) neurons. Cell ionic channel distributions were constrained by literature and optimized to reproduce with high precision
in vitro recordings for these two cell types, which used detailed cell
morphologies with 700 + compartments from morphological reconstructions. The network was driven by the main long-range inputs to
BMC Neurosci 2017, 18(Suppl 1):60
M1: thalamus, primary and secondary somatosensory cortex (S1, S2),
contralateral M1, secondary motor cortex (M2), and orbital cortex (OC).
Local and long-range network connectivity was based on optogenetic
circuit mapping studies which have demonstrated that connection
strength cannot be fully defined by layer identification but depends
strongly on cortical depth and on the subtype of pyramidal cell. Therefore, highly specific synaptic input positional distribution along dendritic trees of these different types were incorporated. We hypothesize
that these distinct patterns of dendritic innervation will have different
effects that reflect roles in multiple co-existing neural coding patterns.
The model was developed using NetPyNE, a novel Python package
that facilitates the development of biological neuronal networks in the
NEURON simulator, and emphasizes the incorporation of multiscale
anatomical and physiological data at varying levels of detail. Parallel
simulations were executed on the XSEDE San Diego Supercomputer
Center. Our M1 model incorporates quantitative experimental data
from 14 publications, therefore accumulating previously isolated
knowledge into a unified framework.
We studied the output of the 15 local M1 populations in response to
increased input from each of the seven long-range inputs modeled.
Sensory information from S1, S2 and sensory thalamus primarily modulated M1 superficial layers, which projected unidirectionally to layer
5B corticospinal neurons. Secondary motor cortical areas, as well as
basal ganglia-relaying thalamic direct inputs, also modulated the same
layer 5B circuits. Firing rates, oscillations, and information transfer
(measured using Granger causality and normalized transfer entropy)
demonstrated differences in dynamics and information flow along the
two parallel pathways is encoded, transformed and integrated. At the
dendritic scale, the distinct innervation patterns in corticospinal neurons facilitated the integration of information from distinct regions,
and the modulatory role of HCN (H current) ion channels, which has
been hypothesized to provide a mechanism to translate action planning into action execution.
Acknowledgements
Research supported by NIH grant U01EB017695, NIH R01EB022903
and NIH R01MH086638.
Page 172 of 176
axon segment, and along the dendrites. In addition, we calculated
resonance strength and phase relationship between input current and
output membrane potential.
The model showed that preferred oscillation frequency depends
critically on parameters defining the ionic conductance of neuronal
compartments that are active in the subthreshold range, which drive
currents that contribute to the total membrane potential. These ionic
currents include: hyperpolarization-activated anomalous rectifier, lowthreshold inactivating calcium, persistent sodium, transient inactivating potassium, and muscarinic potassium (M current). For instance,
resonance at low frequencies (1-4 Hz) can be produced by low-threshold calcium at near-rest/hyperpolarized potentials, resonance at intermediate frequencies (4-10 Hz) can be produced by anomalous rectifier
at near-rest/hyperpolarized potentials, and resonance at intermediate
and faster frequencies (4-30 Hz) is produced by M current from resting
to more depolarized potentials.
The existence of perisomatic and distal dendritic zones whose intrinsic properties preferentially enhance or impede resonance in specific
frequency ranges coincides with differential afferent connectivity [5].
Understanding the relationship between the intrinsic properties of
these zones and the areas of the brain that target them could reveal
additional insight about functional connectivity. Furthermore, the
oscillatory behavior of the zones may modulate action potential initiation at the soma/axon hillock of a neuron which results in variability in
overall network activity.
References
1. Erchova I, Kreck G, Heinemann U, Herz AVM: Dynamics of rat entorhinal
cortex layer II and III cells: characteristics of membrane potential resonance
at rest predict oscillation properties near threshold, J Physiol 2004, 560.1:
89–110.
2. Yoshida M, Giocomo LM, Boardman I, Hasselmo ME: Frequency of subthreshold oscillations at different membrane potential voltages in neurons
at different anatomical positions on the dorso-ventral axis in the rat medial
entorhinal cortex, J Neurosci 2011, 31(35): 12683–12694.
3. Remme MWH, Lengyel M, Gutkin BS: The role of ongoing dendritic oscillations in single-neuron dynamics, PLoS Comput Biol 2009, 5(9): e1000493.
4. Bower JM, Beeman D: The Book of Genesis, 2nd Edition. New York: SpringerVerlag; 1998.
5. Zhuchkova E, Remme MWH, Schreiber S: Somatic versus dendritic resonance: differential filtering of inputs through non-uniform distributions of
active conductances, PLOS One 2013, 8(11): e78908.
P307
Membrane resonance and oscillation preferences of a
multi‑compartment model pyramidal neuron
Melvin A. Felton Jr1, Alfred B. Yu2, David L. Boothe2, Kelvin S. Oie2, Piotr J.
Franaszczuk2, 3
1
U. S. Army Research Laboratory, Adelphi, MD 20783, USA; 2U. S. Army
Research Laboratory, Aberdeen Proving Ground, MD 21005, USA;
3
Department of Neurology, Johns Hopkins University School of Medicine,
Baltimore, MD 21287, USA
Correspondence: Melvin A. Felton Jr (melvin.a.felton.civ@mail.mil)
BMC Neuroscience 2017, 18 (Suppl 1):P307
P308
Network cloning using DNA barcodes
Sergey A. Shuvaev, Batuhan Başerdem, Anthony Zador, Alexei A. Koulakov
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
Correspondence: Alexei A. Koulakov (koulakov@cshl.edu)
BMC Neuroscience 2017, 18 (Suppl 1):P308
In the soma of neocortical neurons, near-threshold depolarizations
have been shown to induce subthreshold membrane potential oscillations that contribute to network oscillations by enhancing or hindering
neuronal responses to synaptic inputs in specific frequency bands [1].
The frequency of these subthreshold membrane potential oscillations
coincides with the peak resonance of the neuronal membrane. While
differential responses within the soma of neocortical neurons to inputs
of varying frequency have been well studied, the dendritic contribution
within these same neurons is less clear [2], [3]. In addition, the differential impact on neuronal response properties of afferent inputs to different areas or “zones” of pyramidal neurons is not well understood.
We characterize resonance and membrane potential oscillation characteristics of a biophysically-realistic compartmental model of a neocortical layer 5 pyramidal neuron [4]. We simulated injected currents
with varying temporal properties, including both step currents and
sinusoidal currents with linearly increasing frequency (chirp currents),
to determine the resonant properties of individual model compartments that were parameterized to reflect known differences in the
properties of functionally distinct zones. We computed changes in
membrane potential under different conditions of input current,
and calculated the input and transfer impedance in the soma, initial
The ability to measure or manipulate network connectivity is the main
challenge in the field of connectomics. Recently, a set of approaches
has been developed that takes advantage of next generation DNA
sequencing to scan connections between neurons into a set of DNA
barcodes. Individual DNA sequences called markers represent single
neurons, while pairs of markers, called barcodes contain information
about connections. Here we propose a strategy for ‘copying’ or ‘cloning’ connectivity contained in barcodes into a clean slate tabula rasa
network. We show that a one marker one cell (OMOC) rule, which
forces all markers with the same sequence to condense into the same
neuron, leads to fast and reliable formation of desired connectivity
in a new network. We show that OMOC rule yields convergence in a
number of steps given by a power law function of the network size. We
thus propose that copying network connectivity from one network to
another is theoretically possible. Most current implementations of artificial neural networks are on digital computers and GPUs [1]. On these
architectures, connections are stored explicitly and therefore straightforward to extract and copy into a new network. However, in biological networks, there is no central repository for connections, so reading
out the connections of a network and copying them into a new network represents a difficult challenge.
BMC Neurosci 2017, 18(Suppl 1):60
Figure 1. Network cloning as a way to copy connectivity from one
network to another. The original network is read out into a set of
barcodes carrying information about connections. Each half of the
barcode (marker) represents one of the cells that are connected,
while the link represents the direction of the connections. These
barcodes are then introduced into a tabula rasa network that has
no structure. Barcodes are capable to shape the tabula rasa network to match the target connectivity
We have recently proposed a new way to read out neuronal connections using DNA barcodes [2, 3]. In this strategy, individual neurons
produce distinguishable pseudo-random DNA identifiers called
markers. Pairs of markers, called here barcodes, represent individual
synaptic connections (Figure 1). Barcodes are read out using highthroughput sequencing technology, either in situ [4] or ex vivo after
individual neurons are disassociated. This strategy allows to convert
connections between neurons into an ensemble of DNA barcodes
that can be identified using sequencing methods. Here we formulate
a different question: Given an ensemble of connections represented
by barcodes, can we copy them into a new network? In other words,
can original network be cloned? We explore a computational model
that simulates the behavior of barcodes introduced into a tabula rasa
network with unstructured connectivity and test its ability to recreate target connectivity in such networks (Fig. 1). We require that the
underlying mechanisms be purely local, i.e. the behavior of each cell
and barcode is based on the information available in this cell or in its
synapses only. The particular form of dynamics that we considered is
described by one marker one cell rule (OMOC), which favors positioning of a single type of marker DNA sequence in a single neuron. We
showed that OMOC dynamics leads to fast and reliable recreation of
desired connectivity in the new network. The formation of new connectivity is achieved in a number of steps given by a power law of the
network size. Thus, copying connectivity from one neural network to
another using DNA barcodes is theoretically possible.
References
1. LeCun, Y., Y. Bengio, and G. Hinton: Deep learning. Nature 2015, 521:
436–44.
2. Kebschull, J.M., et al.: High-Throughput Mapping of Single-Neuron Projections by Sequencing of Barcoded RNA. Neuron 2016, 91: 975–87.
3. Zador, A.M., et al.: Sequencing the connectome. PLoS Biol 2012, 10:
e1001411.
4. Lee, J.H., et al.: Fluorescent in situ sequencing (FISSEQ) of RNA for gene
expression profiling in intact cells and tissues. Nat Protoc 2015, 10: 442–58.
P309
Triads of synchronized theta cycles boost Cross‑Frequency Coupling
during novelty exploration
Víctor J. López-Madrona1, Ernesto Pereda2, Claudio R. Mirasso3,
and Santiago Canals1
1
Instituto de Neurociencias, Consejo Superior de Investigaciones
Científicas, Universidad Miguel Hernández, Sant Joan d’Alacant 03550,
Spain; 2Departamento de Ingeniería Industrial, Escuela Superior de
Ingeniería y Tecnología, Universidad de La Laguna Avda. Astrofísico
Fco. Sanchez, s/n, La Laguna, Tenerife 38205, Spain; 3Instituto de Física
Interdisciplinar y Sistemas Complejos, CSIC-UIB, Campus Universitat de
les Illes Balears E-07122, Palma de Mallorca, Spain
Correspondence: Víctor J. López-Madrona (v.lopez@umh.es)
BMC Neuroscience 2017, 18 (Suppl 1):P309
Page 173 of 176
Introduction: A prominent feature of brain activity is the presence
of oscillations in neuronal recordings. Interactions between brain
rhythms have been demonstrated at different space and time scales,
and are believed to play an important role in neuronal communication. One such interactions is the cross-frequency coupling (CFC)
between the phase of the theta rhythm and the amplitude of gamma
oscillations in the hippocampus. However, neither the cellular mechanisms supporting this interaction, nor its behavioral correlates, are
well understood. In the present work, we study the causal interactions between both frequency bands and the features maximizing this
interaction in the hippocampus of behaving animals.
Methods: Sprague-Dawley rats (n = 5) were used for multi-site, multichannel electrophysiological recordings in freely moving conditions.
Local field potentials (LFP) were recorded across the CA1 and dentate
gyrus (DG) regions of the dorsal hippocampus. We applied an independent component analysis to dissect the local generators of the LFP
signals. CFC between theta phase and gamma amplitude was computed and the directionality of this modulation measured with phase
transfer entropy (PhTE). Alterations on the level of coupling were estimated as a function of the synchronization between the theta phases
of the generators and in relation to the exploration of new vs. known
environments (novelty test) or new vs. known object locations in a
familiar environment (novel object location task or NOL).
Results: Phase differences between theta oscillations across hippocampal subfields were patent, as expected from previous literature.
PhTE analysis indicates a causal link between theta and gamma bands
in each component, suggesting that theta phases modulate gamma
amplitudes and no otherwise. We show that CFC in each LFP generator
is predominantly found when theta phases are synchronized (locked
phase-differences) across hippocampal subfields, at least during three
consecutive cycles. However, maximal gamma amplitude is found
from the first cycle in the triad. The transitions between synchronization states were analyzed through Markov chains and found a significantly higher probability of phase-locking in CA1 and CA3 previous to
a global synchronization state. Importantly, quantification of the modulation index during behavior demonstrates maximal theta-gamma
coupling when the subject is exposed to a novel environment or when
the animal explores a new object location in the NOL task.
Conclusions: Our findings suggest that CFC is a communication
mechanism in the hippocampus for encoding new information into
memory. Efficient coupling requires precise and sustained synchronization across all subfields, suggesting global integration.
P310
Simulations of synaptic integration in a detailed Purkinje cell model
Stefano Masoli1, Egidio D’Angelo1,2
1
Department of Brain and Behavioural Science, Neurophysiology
and Neurocomputation Unit, University of Pavia, Via Forlanini 6, I-27100,
Pavia, Italy; 2Brain Connectivity Center, Istituto Neurologico IRCCS C.
Mondino, Pavia, I-27100, Italy
Correspondence: Stefano Masoli (stefano.masoli@unipv.it)
BMC Neuroscience 2017, 18 (Suppl 1):P310
The Purkinje cell (PC) is one of the most complex neurons of the
brain and integrates more than 100000 synaptic inputs coming from
granule cell (GrC) ascending axons (aa), parallel fibers (pf ), climbing
fibers (cf ) and molecular layer interneurons (mli). The synapses are
distributed, with zone specific limitations, on a large dendritic tree
and exploit different neurotransmission mechanisms to modulate
the PC discharge in way that remains largely unknown. Here we have
explored this wide synaptic computational space using a detailed PC
model built in Python - NEURON [1]. The GrC inputs generated the
characteristic burst-pause in the simple spike (SS) responses that were
accentuated by MLI inhibition [2]. The cf inputs elicited complex spikes
(CS) followed by pauses in SS firing that inversely correlated with the
number of spikes in cf bursts [3]. Ephaptic coupling between MLIs and
PCs depressed SS firing, especially when associated to GABA-A receptor-mediated synaptic inputs to the PC soma. The activation of GABA-B
receptors and Kir channels generated a permanent downstate [4]. The
PC discharge patterns depended on the excitatory/inhibitory balance,
BMC Neurosci 2017, 18(Suppl 1):60
efficacy, dendritic location, Zebrin (Z + vs. Z-) phenotype, and input
patterns of the synapses in a way that matched a large set of experimental observations [5]. The model thus anticipates how a large set of
electroresponsive patterns could emerge from the complexity of PCs
synaptic organization generating the specific outputs to be transmitted to DCN [6].
References
1. Masoli S, Solinas S, D’Angelo E. Action potential processing in a detailed
Purkinje cell model reveals a critical role for axonal compartmentalization.
Front. Cell. Neurosci. 2015; 9:1–22.
2. Steuber V, Mittmann W, Hoebeek FE, Silver RA, De Zeeuw CI, Häusser M,
et al. Cerebellar LTD and pattern recognition by Purkinje cells. Neuron. 2007;
54:121–36.
3. Yartsev MM, Givon-Mayo R, Maller M, Donchin O. Pausing purkinje cells in
the cerebellum of the awake cat. Front. Syst. Neurosci. 2009; 3:2.
4. Loewenstein Y, Mahon S, Chadderton P, Kitamura K, Sompolinsky H, Yarom
Y, et al. Bistability of cerebellar Purkinje cells modulated by sensory stimulation. Nat. Neurosci. 2005; 202–11.
5. Zhou H, Voges K, Lin Z, Ju C, Schonewille M. Differential Purkinje cell simple
spike activity and pausing behavior related to cerebellar modules. J. Neurophysiol. 2015; 113:2524–36.
6. Dykstra S, Turner RW. Determinants of rebound burst responses in rat
cerebellar nuclear neurons to physiological stimuli Steven. Mol. Microbiol.
2015; 82:1496–514.
P311
A model for tactile stimuli processing in cuneate nucleus
Udaya B. Rongala1, Alberto Mazzoni1†, Anton Spanne2, Henrik Jorntell2†,
Calogero M. Oddo1†
1
The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera,
Pisa 56025, Italy; 2Neural Basis of Sensorimotor Control, Department
of Experimental Medical Science, Lund University, Lund, Sweden
Correspondence: Alberto Mazzoni (alberto.mazzoni@santannapisa.it)
†
equal last author contribution
BMC Neuroscience 2017, 18 (Suppl 1):P311
Our understanding of tactile information processing in humans made
critical advancements in the recent years [1] These studies fruitfully
interacted with those aiming at the development of neuromorphic
devices [2]. Here we tackle this issue combining computational neuroscience and neuroengineering. We modelled tactile sensors and
neurons from both the periphery and the cuneate nucleus taking
advantage of the existing neurophysiology knowledge. We injected
them with inputs coming from electronic hardware sensors presented
with a variety of artificial and naturalistic textures. This approach offers
rewards in making robots efficient [3], and contributing for better
understanding of neural mechanisms of sensory processing [4].
First, we generated artificial mechanoreceptor-like output injecting the
output of our biomimetic tactile sensor into an Izhikevich regular spiking neuron. We injected the normalized output and its derivative to
reproduce the dynamics of Slowly Adaptive and Fast Adaptive neurons
respectively [1]. We mimicked the rich information content in primary
afferent sensors by presenting 10 naturalistic textures (Glass, BioSkin,
Textiles, etc.) to our tactile sensor, in a passive touch protocol. We have
achieved accuracy as high as 97% in classifying these 10 textures using
a kNN decoding based on Victor-Purpura distances [5]. As a second
step, we have simulated a second layer of neurons receiving the output
of mechanosensors with conduction delays mimicking the peripheral
nerve fibers that transmit primary afferent signals onto the cuneate neurons (CNs). The CNs are the second order neuron structure present in the
brain stem [1, 2], that is responsible in segregating the PA information
based on different tactile input features [6]. The conduction delays generated a structure of coincident inputs that could encode stimuli orientation [7]. We modeled then the learning of stimuli segregation in CNs,
based on recent neurophysiology studies [6, 8]. We developed a model
of synaptic learning plasticity able to reproduce the sparseness of CNs
encoding of information from primary afferent. We tested this model by
presenting a broad spectrum of high & low frequency inputs (textures
& shape stimuli, using sliding & indentation protocol respectively) in a
Page 174 of 176
pseudo random fashion to induce a realistic rearrangement of synaptic
weights, studying the evolution of connectivity with stimulation history.
We found that highly specialized CNs tended to pick up diverse features
in the input spike patterns and hence in tactile stimuli. Our model provides a candidate mechanism for feature extraction in CNs and might
pave the way to neuromorphic algorithms able to learn to segregate
tactile inputs.
Acknowledgements
This work was supported by institutional funds from Scuola Superiore
Sant’Anna, and by the Ministero degli Affari Esteri e della Cooperazione
Internazionale, via the Italy-Sweden bilateral research projectSE14GR4.
References
1. Johansson RS, Flanagan JR: Coding and use of tactile signals from the
fingertips in object manipulation tasks. Nature Reviews Neuroscience2009,
10: 345–359.
2. Bologna LL, Pinoteau J, Passot JB, Garrido JA, Vogel J, Vidal ER, Arleo A:A
closed-loop neurobotic system for fine touch sensing. Journal of neural
engineering 2013, 10(4):046019.
3. ServiceRF: Minds of their own. Science2014, 346.6206:182-183.
4. Oddo CM, Raspopovic S, Artoni F, Mazzoni A, Spigler G, Petrini F, Giambattistelli F, Vecchio F, Miraglia F, Zollo L, et al.: Intraneural stimulation elicits
discrimination of textural features by artificial fingertip in intact and amputee humans. Elife 2016, 5:e09148.
5. Rongala UB, Mazzoni A, Oddo CM: Neuromorphic artificial touch for categorization of naturalistic textures. IEEE transactions on neural networks and
learning systems 2015, PP(99): 1–1
6. Jörntell H, Bengtsson F, Geborek P, Spanne A, Terekhov AV, Hayward V: Segregation of tactile input features in neurons of the cuneate nucleus. Neuron
2014, 83(6):1444–52.
7. Rongala UB, Mazzoni A, Camboni D, Carrozza MC, Oddo CM: Neuromorphic
artificial sense of touch: Bridging robotics and neuroscience. Robotics
Research, Springer Proceedings in Advanced Robotics 2017.
8. Bengtsson F, Brasselet R, Johansson RS, Arleo A, Jörntell H: Integration
of sensory quanta in cuneate nucleus neurons in vivo. PloS one 2013,
8(2):e56630.
P312
A new method of system visualization of cognitive functioning
for fMRI
Alexander V. Vartanov1, Anastasia K. Neklyudova1, Stanislav A. Kozlovskiy1,
Andrey A. Kiselnikov1, Julia A. Marakshina1,2
1
Department of Psychology, Lomonosov Moscow State University,
Moscow, Russia; 2Psychological Institute of Russian Academy
of Education, Moscow, Russia
Correspondence: Alexander V. Vartanov (a_v_vartanov@mail.ru)
BMC Neuroscience 2017, 18 (Suppl 1):P312
There are two main ways of neural networks identification in human
brain using fMRI: based on searching for dependency of voxel activity on the task performance and based on finding interdependency
of voxel activity among each other (i.e., resting state) [1]. The disadvantage of the first method is that it is practically impossible to select
an experimental task which only one cognitive process is involved in,
and that is why it is difficult to talk about specificity of the obtained
network to some particular function. Because of this item, it is necessary to conduct a system of tasks. Brain activity which was obtained
using the second way also connects not only with certain cognitive
processes, but reflects primarily default state of the brain. Furthermore, another serious problem is artifacts and physiological noise [2].
A new method of system visualization of cognitive functioning for
fMRI (Russian Federation #2016149614 patent pending) and corresponding software (Fact-fMRI) for complex factor analysis for several
individual datasets is presented (Fig. 1). Compared to the ICA method,
the new method allows to estimate the number of functional systems
by orthogonal factors which can be additionally rotated. This method
allows to formalize identification of brain systems which are involved
in executing of different cognitive tasks. A proposed method includes
the following stages:
BMC Neurosci 2017, 18(Suppl 1):60
1. Normalization of voxel activity in a given time slice in a specific
task;
2. Obtaining a matrix of voxel activity in time slices in each of the
tasks;
3. Calculating cross-correlation between different time slices within
one task and among the others and the following factorization (Q
factor analysis);
4. Dimensionality estimation of this matrix based on eigenvalues;
5. Obtaining the orthogonal system of factors (or factor loadings) as
the result of different types of rotation;
6. Computing a corresponding matrix of factor scores.
The dimensionality of the obtained matrix is interpreted as an amount
of functional systems involved in all conducted cognitive tasks. Factor
loadings are interpreted as characteristic of dynamics of each system
as a whole in different tasks. Factor scores reveal the localization of
each brain system.
Figure 1. A scheme of the Fact-fMRI method. N - a number of cognitive tasks; m - a general number of scans within N tasks; n - a
number of analyzing voxels in the brain; Z - a normalized matrix of
initial data; K - a number of obtained factors, which interpreted as
an amount of functional systems included in all cognitive tasks; A
(rot) - a matrix of factor loadings after rotation, describing dynamics of brain activity for each of functional systems; P - factor scores,
revealing the localization of each brain system
Thus, the proposed method allows to combine data which show conducting several cognitive tasks by a person in a single model. Furthermore, it allows to identify and characterize corresponding functional
brain systems.
Page 175 of 176
The local field potential (LFP) is a widely-used signal to monitor the
activity of neural populations. It is usually considered to be generated
by the synaptic currents triggered by pre-synaptic action potentials.
Nevertheless, the magnitude and spatial distribution of LFP depends
greatly on the anatomy of recorded region of the brain, including the
neuron morphology, arrangement of different neuron types and the
distribution of excitatory and inhibitory synapses.
In hippocampus, the pyramidal cells are aligned with somas placed
in the stratum pyramidale (s.p), basal dendrites in stratum oriens (s.o)
and apical dendrites stretching through stratum lucidum (s.l), stratum
radiatum (s.r) and stratum lacunosum moleculare (s.lm). Distribution of
synapses terminating on the pyramidal cells is also framed. Synapses of
the basket cells terminate mostly on or near to the soma of pyramidal
cells forming basket-like-looking dense axonal structures [1, 2]. Pyramidal cells, on the other hand, stretch their axons much further from the
cell body and terminate their synapses in the s.r and s.o. Extracellular
recordings from the pyramidal cell layer, besides spiking activity, show
very distinct inhibitory fields generated by single interneurons [3, 4].
In neocortex, on the other hand, neurons are positioned in parallel,
they are shifted in the vertical axis. Although, inhibitory synapses are
still placed on nearby somas, they generate effectively closed-field
symmetry, which does not produce large far-field potentials. Recently,
we have shown, however, that the electric field following a single
interneuron spike dominates the on-going LFP. Excitatory neurons
contribute to the LFP with longer latencies suggesting that their contribution is di-synaptic, mediated by an intermediary interneuron [5].
In the present study, we are investigating this discrepancy using computational modelling. We place pyramidal neurons according to their
distribution in the hippocampus and neocortex and activate inhibitory
or excitatory synapses on them. We follow the distributions of neurons
and synapses found in the literature and realistic morphology downloaded from online databases (neuromorpho.org). The simulation of
the model is performed in NEURON simulator through its Python interface and the extracellular field is calculated using the NeuroEAP library
[6]. We reproduce the findings of Bazelot et al. [4] in the hippocampus
and Telenczuk et al. [5] in the neocortex and show that the difference in
the field magnitude originate from the differences in the distribution
of synaptic target of inhibitory and excitatory neurons. Importantly, we
find that the magnitude of the LFP generated by the synapses and the
relative contribution of excitatory vs. inhibitory pre-synaptic neurons
depend on the cortical layer and the source of feedforward activation.
Acknowledgements
We acknowledge support from the European Commission (The
Human Brain Project, grant number: H2020-720270) and Centre
National de la Recherche Scientifique (CNRS, France).
References
1. Biswal BB: Resting state fMRI: a personal history. Neuroimage, 2012, 62(2):
938–944.
2. Birn RM, Murphy K, Bandettini PA: The effect of respiration variations on
independent component analysis results of resting state functional connectivity. Human brain mapping, 2008, 29(7): 740–750.
1. Miles R, Tóth K, Gulyás AI, Hájos N, Freund TF. Differences between somatic
and dendritic inhibition in the hippocampus. Neuron 1996; 16:815–23.
2. Le Duigou C, Simonnet J, Telenczuk M, Fricker D, Miles RM. Recurrent
synapses and circuits in the CA3 region of the hippocampus: an associative
network. Front. Cell. Neurosci. 2014; 7:262.
3. Glickfeld LL, Roberts JD, Somogyi P, Scanziani M. Interneurons hyperpolarize pyramidal cells along their entire somatodendritic axis. Nat. Neurosci.
2009; 12:21–3.
4. Bazelot M, Dinocourt C, Cohen I, Miles R. Unitary inhibitory field potentials
in the CA3 region of rat hippocampus. J. Physiol. 2010; 588:2077–90.
5. Teleńczuk B, Dehghani N, Quyen MLV, Cash SS, Halgren E, Hatsopoulos NG,
et al. Local field potentials primarily reflect inhibitory neuron activity in
human and monkey cortex. Sci. Rep. 2017; 7:40211.
6. Telenczuk B, Telenczuk M. NeuronEAP library. Zenodo. 2016.
P313
Synaptic distribution predicts unitary LFP fields in the hippocampus
and in the neocortex
Maria Teleńczuk1,2, Bartosz Teleńczuk1,2, Alain Destexhe1,2
1
European Institute for Theoretical Neuroscience, Paris, France; 2Unité de
Neurosciences, Information et Complexité, Gif-sur-Yvette, France
Correspondence: Maria Teleńczuk (maria@telenczuk.pl)
BMC Neuroscience 2017, 18 (Suppl 1):P313
P314
Differential tuning of the low‑ and high‑frequency components
of the neurophonic spectrum reveals the spike contribution of barn
owl’s nucleus laminaris neurons
Paula T. Kuokkanen1,2,3, Anna Kraemer3, Thomas McColgan1, Catherine E.
Carr3, Richard Kempter1,2
1
Inst. For Theoretical Biology, Humboldt-Universitaet Zu Berlin, 10115
Berlin, Germany; 2Bernstein Center for Computational Neuroscience
Acknowledgements
The research was supported by the Russian Science Foundation, project № 16-18-00066.
References
BMC Neurosci 2017, 18(Suppl 1):60
Berlin, 10115 Berlin, Germany; 3Dept. of Biol., Univ. of Maryland, College
Park, MD 20742, USA
Correspondence: Paula T. Kuokkanen (p.kuokkanen@biologie.hu-berlin.
de)
BMC Neuroscience 2017, 18 (Suppl 1):P314
In-vivo neural activity gives rise to transmembrane currents that
can be recorded as an extracellular field potential. These potentials
are often challenging to interpret due to thousands of contributing
sources. We aim at revealing the neural sources of the “neurophonic”.
The neurophonic is a frequency-following extracellular potential that
can be recorded in the network formed by the nucleus magnocellularis (NM) and the nucleus laminaris (NL) in the brainstem of the barn
owl. NL anatomy is well understood, and putative generators of the
neurophonic are the activity of afferent axons from NM, the synaptic
activation onto NL neurons, and spikes of NL neurons.
We recorded the neurophonic in response to binaural high-frequency
tones (3-7 kHz) close to the recording site’s best frequency, and we varied the interaural time difference (ITD). The mean activity of the monaural inputs to NL does not change with ITD. However, their relative
phase does, causing cancellation or summation of input signals. The
activity of the binaurally sensitive output of NL, i.e., firing rate of NL
neurons, strongly depends on ITD. Our recordings contained both of
these signals, and we analysed the broadband power spectrum of the
response (0-18 kHz).
Page 176 of 176
The low-frequency component (LFc, 200-700 Hz) of the neurophonic
spectrum depended on ITD. The spectrum of extracellularly recorded
NL neurons’ action potentials closely resembled this component. Thus,
the LFc reflects the contribution of action potentials initiated in NL
neurons. The spectral component at the stimulus frequency (SFc) was
much stronger than the LFc. The SFc also depended on ITD, reflecting
the activity of the inputs and their relative phase change with ITD. The
power spectrum at other frequencies did not depend on ITD. We used
the LFc as a proxy for NL neurons’ local population activity, and the SFc
as a proxy for NM axons’ local population activity. We compared the
ITD and frequency tunings of these proxies at each recording site. The
best ITDs of the LFc and the SFc were independent. Also, the tuning
to stimulus frequency was different: LFcs showed typically a 400 Hz
lower best frequency than SFcs. Both findings indicate that the LFc
might originate from NL neurons’ axons in the vicinity of the electrode.
Related NL neurons can be located tens to hundreds of micrometers
away. The findings are consistent with the known anatomy of NL.
Our analysis thus reveals the small contribution of NL neurons to the
neurophonic, improving our understanding of the extracellular field
potential in the auditory brainstem.
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