A discrete time neural network model with spiking neurons II. Dynamics with noise

Bruno Cessac 1
1 NEUROMATHCOMP
CRISAM - Inria Sophia Antipolis - Méditerranée , INRIA Rocquencourt, ENS Paris - École normale supérieure - Paris, UNS - Université Nice Sophia Antipolis, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : We provide rigorous and exact results characterizing the statistics of spike trains in a network of leaky integrate and fire neurons, where time is discrete and where neurons are submitted to noise, without restriction on the synaptic weights. We show the existence and uniqueness of an invariant measure of Gibbs type and discuss its properties. We also discuss Markovian approximations and relate them to the approaches currently used in computational neuroscience to analyse experimental spike trains statistics.
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Article dans une revue
Journal of Mathematical Biology, Springer Verlag (Germany), 2011, pp.863-900. 〈10.1007/s00285-010-0358-4〉
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Contributeur : Bruno Cessac <>
Soumis le : mercredi 27 octobre 2010 - 14:27:05
Dernière modification le : jeudi 26 avril 2018 - 10:28:53

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Bruno Cessac. A discrete time neural network model with spiking neurons II. Dynamics with noise. Journal of Mathematical Biology, Springer Verlag (Germany), 2011, pp.863-900. 〈10.1007/s00285-010-0358-4〉. 〈inria-00530115〉

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