Models 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?
- Laurent Badel
- Magnus Richardson
- Yuval Aviel
- Igor Belikh
- Renaud Jolivet
- Julien Mayor
- Arnaud Tonnelier
- Mona Spiridon
- Alix Herrmann
- Werner M. Kistler
W. Gerstner, R.
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
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
of the lab
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