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dc.creatorMontangie, Lisandro-
dc.creatorMontani, Fernando Fabián-
dc.date2018-06-08T18:33:13Z-
dc.date2018-06-08T18:33:13Z-
dc.date2017-04-
dc.date2018-06-08T14:28:24Z-
dc.date.accessioned2019-04-29T15:44:07Z-
dc.date.available2019-04-29T15:44:07Z-
dc.date.issued2018-06-08T18:33:13Z-
dc.date.issued2018-06-08T18:33:13Z-
dc.date.issued2017-04-
dc.date.issued2018-06-08T14:28:24Z-
dc.identifierMontangie, Lisandro; Montani, Fernando Fabián; Higher-order correlations in common input shapes the output spiking activity of a neural population; Elsevier Science; Physica A: Statistical Mechanics and its Applications; 471; 4-2017; 845-861-
dc.identifier0378-4371-
dc.identifierhttp://hdl.handle.net/11336/47925-
dc.identifierCONICET Digital-
dc.identifierCONICET-
dc.identifier.urihttp://rodna.bn.gov.ar:8080/jspui/handle/bnmm/300646-
dc.descriptionRecent neurophysiological experiments suggest that populations of neurons use a computational scheme in which spike timing is regulated by common non-Gaussian inputs across neurons. The presence of beyond-pairwise correlations in the neuronal inputs and the spiking outputs following a non-Gaussian statistics elicits the need of developing a new theoretical framework taking into account the complexity of synchronous activity patterns. To this end, we quantify the amount of higher-order correlations in the common neuronal inputs and outputs of a population of neurons. We provide a novel formalism, of easy numerical implementation, that can capture the subtle changes of the inputs heterogeneities. Within our approach, correlations across neurons arise from q-Gaussian inputs into threshold neurons and higher-order correlations in the spiking outputs activity are quantified by the parameter q. We present an exhaustive analysis of how input statistics are transformed in this threshold process into output statistics, and we show under which conditions higher-order correlations can lead to either bigger or smaller number of synchronized spikes in the neural population outputs.-
dc.descriptionFil: Montangie, Lisandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina-
dc.descriptionFil: Montani, Fernando Fabián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física de Líquidos y Sistemas Biológicos. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física de Líquidos y Sistemas Biológicos; Argentina-
dc.formatapplication/pdf-
dc.formatapplication/pdf-
dc.languageeng-
dc.publisherElsevier Science-
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/https://dx.doi.org/10.1016/j.physa.2016.12.002-
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0378437116309736-
dc.rightsinfo:eu-repo/semantics/restrictedAccess-
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/-
dc.sourcereponame:CONICET Digital (CONICET)-
dc.sourceinstname:Consejo Nacional de Investigaciones Científicas y Técnicas-
dc.sourceinstacron:CONICET-
dc.subjectEXTENDED CENTRAL LIMIT THEOREM-
dc.subjectHIGHER-ORDER CORRELATIONS-
dc.subjectINFORMATION GEOMETRY-
dc.subjectLARGE NEURAL ENSEMBLE-
dc.subjectNEURONAL INPUTS-
dc.subjectSPIKING OUTPUTS-
dc.subjectAstronomía-
dc.subjectCiencias Físicas-
dc.subjectCIENCIAS NATURALES Y EXACTAS-
dc.titleHigher-order correlations in common input shapes the output spiking activity of a neural population-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.typeinfo:ar-repo/semantics/articulo-
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