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

Bruno Cessac (1) , Adrian Palacios (2)

1 NEUROMATHCOMP - Neurosciences Mathématique et Computationnelle
2 CINV - Centro Interdisciplinario de Neurociencia de Valparaiso

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.

Mots clés: retina; Neural Networks; Spike statitsics

Domaines: Systèmes dynamiques [math.DS]; Neurobiologie

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⟩