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


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Bruno Cessac , Adrian G. Palacios

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. More... »

PAGES

261-302

Book

TITLE

Modeling in Computational Biology and Biomedicine

ISBN

978-3-642-31207-6
978-3-642-31208-3

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-31208-3_8

DOI

http://dx.doi.org/10.1007/978-3-642-31208-3_8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049713364


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