Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track: session 2. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track: session 1. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track: session 3. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track: session 2. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track: session 1. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel square track af... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel square track after recovery from medial septum inactivated by muscimol. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel square track, m... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel square track, medial septum inactivated by muscimol. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of square track after an... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of square track after animal recovered from medial septum inactivated by muscimol. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track: session 3. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel square track, m... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel square track, medial septum inactivated by muscimol. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Place cells of the rodent hippocampus fire action potentials when the animal traverses a particul... more Place cells of the rodent hippocampus fire action potentials when the animal traverses a particular spatial location in any environment. Therefore for any given trajectory one observes a repeatable sequence of place cell activations. When the animal is quiescent or sleeping, one can observe similar sequences of activation known as replay, which underlie the process of memory consolidation. However, it remains unclear how replay is generated. Here we show how a temporally asymmetric plasticity rule during spatial exploration gives rise to spontaneous replay in a model network by shaping the recurrent connectivity to reflect the topology of the learned environment. Crucially, the rate of this encoding is strongly modulated by ongoing rhythms. Oscillations in the theta range optimize learning by generating repeated pre-post pairings on a time-scale commensurate with the window for plasticity, while lower and higher frequencies generate learning rates which are lower by orders of magnitude
Understanding the principles of neuronal connectivity requires tools for efficient quantification... more Understanding the principles of neuronal connectivity requires tools for efficient quantification and visualization of large datasets. The primate cortex is particularly challenging due to its complex mosaic of areas, which in many cases lack clear boundaries. Here, we introduce a resource that allows exploration of results of 143 retrograde tracer injections in the marmoset neocortex. Data obtained in different animals are registered to a common stereotaxic space using an algorithm guided by expert delineation of histological borders, allowing accurate assignment of connections to areas despite interindividual variability. The resource incorporates tools for analyses relative to cytoarchitectural areas, including statistical properties such as the fraction of labeled neurons and the percentage of supragranular neurons. It also provides purely spatial (parcellation-free) data, based on the stereotaxic coordinates of 2 million labeled neurons. This resource helps bridge the gap betwe...
The marmoset monkey has become an important primate model in Neuroscience. Here we characterize s... more The marmoset monkey has become an important primate model in Neuroscience. Here we characterize salient statistical properties of inter-areal connections of the marmoset cerebral cortex, using data from retrograde tracer injections. We found that the connectivity weights are highly heterogeneous, spanning five orders of magnitude, and are log-normally distributed. The cortico-cortical network is dense, heterogeneous and has high specificity. The reciprocal connections are the most prominent and the probability of connection between two areas decays with their functional dissimilarity. The laminar dependence of connections defines a hierarchical network correlated with microstructural properties of each area. The marmoset connectome reveals parallel streams associated with different sensory systems. Finally, the connectome is spatially embedded with a characteristic length that obeys a power law as a function of brain volume across species. These findings provide a connectomic basis ...
Place cells of the rodent hippocampus fire action potentials when the animal traverses a particul... more Place cells of the rodent hippocampus fire action potentials when the animal traverses a particular spatial location in any environment. Therefore for any given trajectory one observes a repeatable sequence of place cell activations. When the animal is quiescent or sleeping, one can observe similar sequences of activation known as replay, which underlie the process of memory consolidation. However, it remains unclear how replay is generated. Here we show how a temporally asymmetric plasticity rule during spatial exploration gives rise to spontaneous replay in a model network by shaping the recurrent connectivity to reflect the topology of the learned environment. Crucially, the rate of this encoding is strongly modulated by ongoing rhythms. Oscillations in the theta range optimize learning by generating repeated pre-post pairings on a time-scale commensurate with the window for plasticity, while lower and higher frequencies generate learning rates which are lower by orders of magnit...
Place cells of the rodent hippocampus fire action potentials when the animal traverses a particul... more Place cells of the rodent hippocampus fire action potentials when the animal traverses a particular spatial location in a given environment. Therefore, for any given trajectory one will observe a repeatable sequence of place cell activations as the animal explores. Interestingly, when the animal is quiescent or sleeping, one can observe similar sequences of activation, although at a highly compressed rate, known as “replays”. It is hypothesized that this replay underlies the process of memory consolidation whereby memories are “transferred” from hippocampus to cortex. However, it remains unclear how the memory of a particular environment is actually encoded in the place cell activity and what the mechanism for replay is. Here we study how plasticity during spatial exploration shapes the patterns of synaptic connectivity in model networks of place cells. Specifically, we show how plasticity leads to the formation of attracting manifolds: patterns of activity which represent the spati...
Recently, there has been an increased interest on the neural mechanisms underlying perceptual dec... more Recently, there has been an increased interest on the neural mechanisms underlying perceptual decision making. However, the effect of neuronal adaptation in this context has not yet been studied. We begin our study by investigating how adaptation can bias perceptual decisions. We considered behavioral data from an experiment on high-level adaptation-related aftereffects in a perceptual decision task with ambiguous stimuli on humans. To understand the driving force behind the perceptual decision process, a biologically inspired cortical network model was used. Two theoretical scenarios arose for explaining the perceptual switch from the category of the adaptor stimulus to the opposite, nonadapted one. One is noise-driven transition due to the probabilistic spike times of neurons and the other is adaptation-driven transition due to afterhyperpolarization currents. With increasing levels of neural adaptation, the system shifts from a noise-driven to an adaptation-driven modus. The beha...
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track: session 2. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track: session 1. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track: session 3. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track: session 2. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track: session 1. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel square track af... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel square track after recovery from medial septum inactivated by muscimol. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel square track, m... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel square track, medial septum inactivated by muscimol. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of square track after an... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of square track after animal recovered from medial septum inactivated by muscimol. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel hexagonal track: session 3. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel square track, m... more Matlab file with multi-unit, lfp and animal position. 35 min exploration of novel square track, medial septum inactivated by muscimol. From Pastalkova lab at Janelia Research Campus, data collected by Yingxue Wang
Place cells of the rodent hippocampus fire action potentials when the animal traverses a particul... more Place cells of the rodent hippocampus fire action potentials when the animal traverses a particular spatial location in any environment. Therefore for any given trajectory one observes a repeatable sequence of place cell activations. When the animal is quiescent or sleeping, one can observe similar sequences of activation known as replay, which underlie the process of memory consolidation. However, it remains unclear how replay is generated. Here we show how a temporally asymmetric plasticity rule during spatial exploration gives rise to spontaneous replay in a model network by shaping the recurrent connectivity to reflect the topology of the learned environment. Crucially, the rate of this encoding is strongly modulated by ongoing rhythms. Oscillations in the theta range optimize learning by generating repeated pre-post pairings on a time-scale commensurate with the window for plasticity, while lower and higher frequencies generate learning rates which are lower by orders of magnitude
Understanding the principles of neuronal connectivity requires tools for efficient quantification... more Understanding the principles of neuronal connectivity requires tools for efficient quantification and visualization of large datasets. The primate cortex is particularly challenging due to its complex mosaic of areas, which in many cases lack clear boundaries. Here, we introduce a resource that allows exploration of results of 143 retrograde tracer injections in the marmoset neocortex. Data obtained in different animals are registered to a common stereotaxic space using an algorithm guided by expert delineation of histological borders, allowing accurate assignment of connections to areas despite interindividual variability. The resource incorporates tools for analyses relative to cytoarchitectural areas, including statistical properties such as the fraction of labeled neurons and the percentage of supragranular neurons. It also provides purely spatial (parcellation-free) data, based on the stereotaxic coordinates of 2 million labeled neurons. This resource helps bridge the gap betwe...
The marmoset monkey has become an important primate model in Neuroscience. Here we characterize s... more The marmoset monkey has become an important primate model in Neuroscience. Here we characterize salient statistical properties of inter-areal connections of the marmoset cerebral cortex, using data from retrograde tracer injections. We found that the connectivity weights are highly heterogeneous, spanning five orders of magnitude, and are log-normally distributed. The cortico-cortical network is dense, heterogeneous and has high specificity. The reciprocal connections are the most prominent and the probability of connection between two areas decays with their functional dissimilarity. The laminar dependence of connections defines a hierarchical network correlated with microstructural properties of each area. The marmoset connectome reveals parallel streams associated with different sensory systems. Finally, the connectome is spatially embedded with a characteristic length that obeys a power law as a function of brain volume across species. These findings provide a connectomic basis ...
Place cells of the rodent hippocampus fire action potentials when the animal traverses a particul... more Place cells of the rodent hippocampus fire action potentials when the animal traverses a particular spatial location in any environment. Therefore for any given trajectory one observes a repeatable sequence of place cell activations. When the animal is quiescent or sleeping, one can observe similar sequences of activation known as replay, which underlie the process of memory consolidation. However, it remains unclear how replay is generated. Here we show how a temporally asymmetric plasticity rule during spatial exploration gives rise to spontaneous replay in a model network by shaping the recurrent connectivity to reflect the topology of the learned environment. Crucially, the rate of this encoding is strongly modulated by ongoing rhythms. Oscillations in the theta range optimize learning by generating repeated pre-post pairings on a time-scale commensurate with the window for plasticity, while lower and higher frequencies generate learning rates which are lower by orders of magnit...
Place cells of the rodent hippocampus fire action potentials when the animal traverses a particul... more Place cells of the rodent hippocampus fire action potentials when the animal traverses a particular spatial location in a given environment. Therefore, for any given trajectory one will observe a repeatable sequence of place cell activations as the animal explores. Interestingly, when the animal is quiescent or sleeping, one can observe similar sequences of activation, although at a highly compressed rate, known as “replays”. It is hypothesized that this replay underlies the process of memory consolidation whereby memories are “transferred” from hippocampus to cortex. However, it remains unclear how the memory of a particular environment is actually encoded in the place cell activity and what the mechanism for replay is. Here we study how plasticity during spatial exploration shapes the patterns of synaptic connectivity in model networks of place cells. Specifically, we show how plasticity leads to the formation of attracting manifolds: patterns of activity which represent the spati...
Recently, there has been an increased interest on the neural mechanisms underlying perceptual dec... more Recently, there has been an increased interest on the neural mechanisms underlying perceptual decision making. However, the effect of neuronal adaptation in this context has not yet been studied. We begin our study by investigating how adaptation can bias perceptual decisions. We considered behavioral data from an experiment on high-level adaptation-related aftereffects in a perceptual decision task with ambiguous stimuli on humans. To understand the driving force behind the perceptual decision process, a biologically inspired cortical network model was used. Two theoretical scenarios arose for explaining the perceptual switch from the category of the adaptor stimulus to the opposite, nonadapted one. One is noise-driven transition due to the probabilistic spike times of neurons and the other is adaptation-driven transition due to afterhyperpolarization currents. With increasing levels of neural adaptation, the system shifts from a noise-driven to an adaptation-driven modus. The beha...
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Papers by Panagiota Theodoni