Dynamics of time elapsed inhomogeneous neuron network model

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dc.contributor.authorKang, Moon-Jinko
dc.contributor.authorPerthame, Benoitko
dc.contributor.authorSalort, Delphineko
dc.date.accessioned2020-12-18T08:50:16Z-
dc.date.available2020-12-18T08:50:16Z-
dc.date.created2020-12-18-
dc.date.issued2015-12-
dc.identifier.citationCOMPTES RENDUS MATHEMATIQUE, v.353, no.12, pp.1111 - 1115-
dc.identifier.issn1631-073X-
dc.identifier.urihttp://hdl.handle.net/10203/278722-
dc.description.abstractModels for neural networks have been proposed, which describe the probability to find a neuron for which time s has elapsed since the last discharge. These are written under the form of a nonlinear age-structured equation where the total network activity modulates the firing rate. Here, we consider an inhomogeneous network with variability on the refractory period. We give conditions on the connectivity, leading to total desynchronization of the network.-
dc.languageEnglish-
dc.publisherELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER-
dc.titleDynamics of time elapsed inhomogeneous neuron network model-
dc.typeArticle-
dc.identifier.wosid000366617600009-
dc.identifier.scopusid2-s2.0-84960927950-
dc.type.rimsART-
dc.citation.volume353-
dc.citation.issue12-
dc.citation.beginningpage1111-
dc.citation.endingpage1115-
dc.citation.publicationnameCOMPTES RENDUS MATHEMATIQUE-
dc.identifier.doi10.1016/j.crma.2015.09.029-
dc.contributor.localauthorKang, Moon-Jin-
dc.contributor.nonIdAuthorPerthame, Benoit-
dc.contributor.nonIdAuthorSalort, Delphine-
dc.description.isOpenAccessY-
dc.type.journalArticleArticle-
dc.subject.keywordPlusPOPULATION-
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