Models of spiking neurons

Standard neural network theory describes the neuron as an input-output unit with a nonlinear transfer function. Real biological neurons are much more complicated than that. They have an intrinsic dynamics which transforms the input into a sequence of electrical pulses, the so-called action potentials or spikes which are transmitted along the axon to other neurons. The exact timing of spikes can, in principle, convey a lot of information which is lost if only the temporally averaged mean firing rate is considered. During recent years, an increasing amount of experimental evidence has accumulated which shows that temporal information plays indeed a role in several biological neural systems.

In this line of research we study neural network models with spiking neurons. We address, among others, the following questions:

  • What is a useful level of description of spiking activity?
  • Do we need a large set of differential equations for every neuron or is a description by a threshold model good enough?
  • What are the global states in large networks of spiking neurons? * How is information transmitted between population of neurons?


Previous collaborators:

  • Richard Naud
  • Laurent Badel
  • Magnus Richardson
  • Yuval Aviel
  • Igor Belikh
  • Renaud Jolivet
  • Julien Mayor
  • Arnaud Tonnelier
  • Mona Spiridon
  • Alix Herrmann
  • Werner M. Kistler


Some publications:

W. Gerstner, R. Naud (2009)
How good are Neuron Models?
Science, Vol. 326, Nr. 5951, pp. 379-380

R. Naud, N. Marcille, C. Clopath and W. Gerstner (2008)
Firing patterns in the adaptive exponential integrate-and-fire model
Biological Cybernetics, Vol. 99, Nr. 4-5, pp. 335-347

L. Badel, W. Gerstner, J. E. Richardson (2008)
Spike-triggered averages for passive and resonant neurons receiving filtered excitatory and inhibitory synaptic drive
PHYSICAL REVIEW E, vol. 78, num. 1

L. Badel, S. Lefort, R. Brette, C. C. H. Petersen, W. Gerstner, M. J. E. Richardson (2008)
Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces
J. Neurophysilogy 99: 656-666

R. Jolivet, R. Kobayashi, A. Rauch, R. Naud, S. Shinomoto, W. Gerstner (2008)
A benchmark test for a quantitative assessment of simple neuron models
J. Neuroscience Methods 169: 417-424

R. Jolivet and T. J. Lewis and W. Gerstner (2004)
Generalized Integrate-and-Fire Models of Neuronal Activity Approximate Spike Trains of a Detailed Model to a High Degree of Accuracy
J. Neurophysiology 92: 959-976

W. Gerstner and W. Kistler
BOOK : `Spiking Neuron Models – Single Neurons, Populations Plasticity
Cambridge Univ. Press (2002)

W. Gerstner (2001).
Coding Properties of Spiking Neurons: Reverse and Cross-Correlations
Neural Networks 14:599-610

A. Herrmann and W. Gerstner (2001a)
Noise and the PSTH response to current transients: I. General theory and application to the integrate-and-fire neuron
Journal of Computational Neuroscience 11:135-151

W. Gerstner (2000)
Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking
Neural Computation 12:43-89.

For additional references, consult the list of Publications of the lab