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dc.creatorCessac, Bruno
dc.creatorPalacios Vargas, Adrian Galo
dc.date.accessioned2016-12-27T21:47:47Z
dc.date.available2016-12-27T21:47:47Z
dc.date.issued2013
dc.identifier.isbn978-3-642-31208-3
dc.identifier.isbn978-3-642-31207-6
dc.identifier.isbn978-3-642-44669-6
dc.identifier.urihttp://hdl.handle.net/10533/164733
dc.description.abstractThis 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.
dc.language.isoeng
dc.relationinstname: Conicyt
dc.relationreponame: Repositorio Digital RI2.0
dc.relationinstname: Conicyt
dc.relationreponame: Repositorio Digital RI 2.0
dc.relation.urihttp://www.springer.com/us/book/9783642312076
dc.titleSPIKE TRAIN STATISTICS FROM EMPIRICAL FACTS TO THEORY: THE CASE OF THE RETINA
dc.typeCapitulo de libro
dc.countryALEMANIA
dc.bibliographicCitation.stpage261
dc.bibliographicCitation.endpage302
dc.identifier.folio1110292
dc.description.conicytprogramFONDECYT
dc.relation.projectidinfo:eu-repo/grantAgreement/Fondecyt/1110292
dc.relation.setinfo:eu-repo/semantics/dataset/hdl.handle.net/10533/93479
dc.rights.driverinfo:eu-repo/semantics/openAccess
dc.type.driverinfo:eu-repo/semantics/bookPart
dc.description.shortconicytprogramFONDECYT
dc.title.libroMODELING IN COMPUTATIONAL BIOLOGY AND BIOMEDICINE. A MULTIDISCIPLINARY ENDEAVOR
dc.creator.libroCazals, Frederic
dc.creator.libroKornprobst, Pierre
dc.description.libropages318
dc.publisher.editorialSPRINGER SCIENCE


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