Abstract
As large-scale, detailed network modeling projects are flourishing in the field of computational neuroscience, it is more and more important to design single neuron models that not only capture qualitative features of real neurons but are quantitatively accurate in silico representations of those. Recent years have seen substantial effort being put in the development of algorithms for the systematic evaluation and optimization of neuron models with respect to electrophysiological data. It is however difficult to compare these methods because of the lack of appropriate benchmark tests. Here, we describe one such effort of providing the community with a standardized set of tests to quantify the performances of single neuron models. Our effort takes the form of a yearly challenge similar to the ones which have been present in the machine learning community for some time. This paper gives an account of the first two challenges which took place in 2007 and 2008 and discusses future directions. The results of the competition suggest that best performance on data obtained from single or double electrode current or conductance injection is achieved by models that combine features of standard leaky integrate-and-fire models with a second variable reflecting adaptation, refractoriness, or a dynamic threshold.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Achard P, De Schutter E (2006) Complex parameter landscape for a complex neuron model. PLoS Comp Biol 2(7): e94
Arcas B, Fairhall A (2003) What causes a neuron to spike. Neural Comp 15: 1789–1807
Aronov D, Victor JD (2004) Non-euclidean properties of spike train metric spaces. Phys Rev E 69: 061,905
Badel L, Lefort S, Brette R, Petersen CCH, Gerstner W, Richardson MJE (2008) Dynamic IV curves are reliable predictors of naturalistic pyramidal-neuron voltage traces. J Neurophysiol 99(2): 656–666
Bower JM, Beeman D (1995) The book of Genesis. Springer, New York
Brette R, Gerstner W (2005) Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity
Brillinger DR (1988a) Maximum likelihood analysis of spike trains of interacting nerve cells. Biol Cybern 59: 189–200
Brillinger DR (1988b) The maximum likelihood approach to the identification of neuronal firing systems. Ann Biomed Eng 16: 3–16
Brillinger DR, Segundo JP (1979) Empirical examination of the threshold model of neuronal firing. Biol Cybern 35: 213–220
Brunel N, Hakim V, Richardson MJE (2003) Firing-rate resonance in a generalized integrate-and-fire neuron with subthreshold resonance. Phys Rev E 67: 051,916
Bush K, Knight J, Anderson C (2005) Optimizing conductance parameters of cortical neural models via electrotonic partitions. Neural Net 18(5–): 488–496
Carandini M, Horton JC, Sincich LC (2007) Thalamic filtering of retinal spike trains by postsynaptic summation. J Vision 7(14): 20
Clopath C, Jolivet R, Rauch A, Lüscher HR, Gerstner W (2007) Predicting neuronal activity with simple models of the threshold type: Adaptive Exponential Integrate-and-Fire model with two compartments. Neurocomputing 70(10–2): 1668–1673
Druckmann S, Banitt Y, Gidon A, Schürmann F, Markram H, Segev I (2007) A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front Neurosci 1: 7–18
Fourcaud-Trocmé N, Hansel D, van Vreeswijk C, Brunel N (2003) How spike generation mechanisms determine the neuronal response to fluctuating inputs. J Neurosci 23: 11,628–11,640
Geisler WS, Albrecht DG, Salvi RJ, Saunders SS (1991) Discrimination performance of single neurons: rate and temporal information. J Neurophysiol 66: 334–362
Gerken WC, Purvis LK, Butera RJ (2006) Genetic algorithm for optimization and specification of a neuron model. Neurocomputing 69(10–2): 1039–1042
Gerstner W, Kistler WM (2002) Spiking neuron models. Cambridge University Press, Cambridge
Gerstner W, Kempter R, van Hemmen JL, Wagner H (1996) A neuronal learning rule for sub-millisecond temporal coding. Nature 383: 76–78
Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol (London) 117: 500–544
Hopfield JJ (1995) Pattern recognition computation using action potential timing for stimulus representation. Nature 376: 33–36
Huys QJM, Ahrens MB, Paninski L (2006) Efficient estimation of detailed single-neuron models. J Neurophysiol 96: 872–890
Izhikevich EM (2004) Which model to use for cortical spiking neurons. IEEE Trans Neural Net 15: 1063–1070
Izhikevich EM, Edelman GM (2008) Large-scale model of mammalian thalamocortical systems. PNAS 105(9): 3593–3598
Jolivet R, Gerstner W (2004) Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival. J Physiol-Paris 98(4–): 442–451
Jolivet R, Lewis TJ, Gerstner W (2004) Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy. J Neurophysiol 92(2): 959–976
Jolivet R, Rauch A, Lüscher HR, Gerstner W (2006a) Integrate-and-Fire models with adaptation are good enough: predicting spike times under random current injection. Adv Neural Inform Process Syst 18: 595–602
Jolivet R, Rauch A, Lüscher HR, Gerstner W (2006b) Predicting spike timing of neocortical pyramidal neurons by simple threshold models. J Comput Neurosci 21(1): 35–49
Jolivet R, Kobayashi R, Rauch A, Naud R, Shinomoto S, Gerstner W (2008) A benchmark test for a quantitative assessment of simple neuron models. J Neurosci Meth 169(2): 417–424
Keat J, Reinagel P, Reid RC, Meister M (2001) Predicting every spike: a model for the responses of visual neurons. Neuron 30: 803–817
Kempter R, Gerstner W, van Hemmen JL, Wagner H (1998) Extracting oscillations: neuronal coincidence detection with noisy periodic spike input. Neural Comp 10: 1987–2017
Keren N, Peled N, Korngreen A (2005) Constraining compartmental models using multiple voltage recordings and genetic algorithms. J Neurophysiol 94(6): 3730–3742
Kistler WM, Gerstner W, van Hemmen JL (1997) Reduction of Hodgkin-Huxley equations to a single-variable threshold model. Neural Comp 9: 1015–1045
Kobayashi R, Shinomoto S (2007) State space method for predicting the spike times of a neuron. Phys Rev E 75(1): 11,925
La Camera G, Rauch A, Lüscher HR, Senn W, Fusi S (2004) Minimal models of adapted neuronal response to in vivo-like input currents. Neural Comp 16: 2101–2124
Lansky P, Sanda P, He J (2006) The parameters of the stochastic leaky integrate-and-fire neuronal model. J Comput Neurosci 21(2): 211–223
Larkum ME, Zhu JJ, Sakmann B (1999) A new cellular mechanism for coupling inputs arriving at different cortical layers. Nature 398(6725): 338–341
Larkum ME, Senn W, Lüscher HR (2004) Top-down dendritic input increases the gain of layer 5 pyramidal neurons. Cereb Cortex 14: 1059–1070
Leibold C, Gundlfinger A, Schmidt R, Thurley K, Schmitz D, Kempter R (2008) Temporal compression mediated by short-term synaptic plasticity. PNAS 105(11): 4417
MacLeod K, Backer A, Laurent G (1998) Who reads temporal information contained across synchronized and oscillatory spike trains. Nature 395: 693–698
Markram H (2006) The blue brain project. Nat Rev Neurosci 7(2): 153–160
Marmarelis VZ, Berger TW (2005) General methodology for nonlinear modeling of neural systems with Poisson point-process inputs. Math Biosci 196(1): 1–13
Mullowney P, Iyengar S (2008) Parameter estimation for a leaky integrate-and-fire neuronal model from ISI data. J Comput Neurosci 24: 179–194
Paninski L, Pillow JW, Simoncelli EP (2005) Comparing integrate-and-fire models estimated using intracellular and extracellular data. Neurocomputing 65(66): 379–385
Pillow JW, Paninski L, Uzzell VJ, Simoncelli EP, Chichilnisky EJ (2005) Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J Neurosci 25(47): 11,003–11,013
Prinz AA, Billimoria CP, Marder E (2003) Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J Neurophysiol 90(6): 3998–4015
Prinz AA, Bucher D, Marder E (2004) Similar network activity from disparate circuit parameters. Nat Neurosci 7(12): 1345–1352
Rauch A, La Camera G, Lüscher HR, Senn W, Fusi S (2003) Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo-like input currents. J Neurophysiol 90: 1598–1612
van Rossum MCW (2001) A novel spike distance. Neural Comp 13: 751–763
Schaefer AT, Larkum ME, Sakmann B, Roth A (2003) Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. J Neurophysiol 89(6): 3143–3154
Song D, Chan RH, Marmarelis VZ, Hampson RE, Deadwyler SA, Berger TW (2007) Nonlinear dynamic modeling of spike train transformations for hippocampal-cortical prostheses. IEEE Trans Biomed Eng 54: 1053–1066
Vanier MC, Bower JM (1999) A comparative survey of automated parameter-search methods for compartmental neural models. J Comput Neurosci 7(2): 149–171
Victor JD, Purpura KP (1996) Nature and precision of temporal coding in visual cortex: a metric-space analysis. J Neurophysiol 76: 1310–1326
Victor JD, Purpura KP (1997) Metric-space analysis of spike trains: theory, algorithms and application. Netw Comp Neural Syst 8: 127–164
Wang Y, Gupta A, Toledo-Rodriguez M, Wu CZ, Markram H (2002) Anatomical, physiological, molecular and circuit properties of nest basket cells in the developing somatosensory cortex. Cereb Cortex 12: 395–410
Weaver CM, Wearne SL (2006) The role of action potential shape and parameter constraints in optimization of compartment models. Neurocomputing 69(10–2): 1053–1057
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jolivet, R., Schürmann, F., Berger, T.K. et al. The quantitative single-neuron modeling competition. Biol Cybern 99, 417–426 (2008). https://doi.org/10.1007/s00422-008-0261-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00422-008-0261-x