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Spike Train Statistics from Empirical Facts to Theory: The Case of the Retina

Bruno Cessac 1, * Adrian Palacios 2 
* Corresponding author
CRISAM - Inria Sophia Antipolis - Méditerranée , INRIA Rocquencourt, ENS-PSL - École normale supérieure - Paris, UNS - Université Nice Sophia Antipolis (1965 - 2019), CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : This chapter focuses on methods from statistical physics and probability theory allowing the analysis of spike trains in neural networks. Taking as an example the retina we present recent works attempting to understand how retina ganglion cells encode the information transmitted to the visual cortex via the optical nerve, by analyzing their spike train statistics. We compare the maximal entropy models used in the literature of retina spike train analysis to rigorous results establishing the exact form of spike train statistics in conductance-based Integrate-and-Fire neural networks.
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Submitted on : Saturday, November 12, 2011 - 8:13:59 PM
Last modification on : Thursday, August 4, 2022 - 4:54:04 PM
Long-term archiving on: : Friday, November 16, 2012 - 10:40:54 AM


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  • HAL Id : hal-00640507, version 1


Bruno Cessac, Adrian Palacios. Spike Train Statistics from Empirical Facts to Theory: The Case of the Retina. Frédéric Cazals and Pierre Kornprobst. Modeling in Computational Biology and Biomedicine: A Multidisciplinary Endeavor, Springer, 2013. ⟨hal-00640507⟩



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