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

In: Modeling in Computational Biology and Biomedicine

Author

Listed:
  • Bruno Cessac

    (Neuromathcomp project-team, Inria Sophia Antipolis Méditerranée)

  • Adrian G. Palacios

    (Universidad de Valparaiso, CINV-Centro Interdisciplinario de Neurociencia de Valparaiso)

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.

Suggested Citation

  • Bruno Cessac & Adrian G. Palacios, 2013. "Spike Train Statistics from Empirical Facts to Theory: The Case of the Retina," Springer Books, in: Frédéric Cazals & Pierre Kornprobst (ed.), Modeling in Computational Biology and Biomedicine, edition 127, chapter 0, pages 261-302, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-31208-3_8
    DOI: 10.1007/978-3-642-31208-3_8
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