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

Bruno Cessac () and Adrian G. Palacios ()
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Bruno Cessac: Neuromathcomp project-team, Inria Sophia Antipolis Méditerranée
Adrian G. Palacios: Universidad de Valparaiso, CINV-Centro Interdisciplinario de Neurociencia de Valparaiso

Chapter Chapter 8 in Modeling in Computational Biology and Biomedicine, 2013, pp 261-302 from Springer

Abstract: 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.

Keywords: Ising Model; Spike Train; Synaptic Weight; Visual Scene; Neural Code (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-31208-3_8

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DOI: 10.1007/978-3-642-31208-3_8

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