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Reconstructing the functional connectivity of multiple spike trains using Hawkes models

dc.contributor.authorLambert, Régis*
dc.contributor.authorTuleau-Malot, Christine*
dc.contributor.authorBessaih, Thomas*
dc.contributor.authorRivoirard, Vincent*
dc.contributor.authorBouret, Yann*
dc.contributor.authorLeresche, Nathalie*
dc.contributor.authorReynaud-Bouret, Patricia*
dc.date.accessioned2018-03-09T14:37:30Z
dc.date.available2018-03-09T14:37:30Z
dc.date.issued2018
dc.identifier.issn0165-0270
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/17535
dc.language.isoenen
dc.subjectconnectivity
dc.subjectspike train analysis
dc.subjectneuron correlation
dc.subjectlasso penalization
dc.subjectleast-square estimation
dc.subjectHawkes processes
dc.subject.ddc520en
dc.titleReconstructing the functional connectivity of multiple spike trains using Hawkes models
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenBackground: Statistical models that predict neuron spike occurrence from the earlier spiking activity of the whole recorded network are promising tools to reconstruct functional connectivity graphs. Some of the previously used methods were in the general statistical framework of the multivariate Hawkes processes but they often required huge amount of data, prior knowledge about the recorded network, and may generate non stationary models that could not be directly used in simulation. New Method: Here, we present a method, based on least-square estimators and LASSO penalty criteria, optimizing Hawkes models that can be used for simulation. Results: Challenging our method to multiple Integrate and Fire models of neuron networks demonstrated that it eciently detects both excitatory and inhibitory connections. The few errors that occasionally occurred with complex networks including common inputs, weak and chained connections, could easily be discarded based on objective criteria. Conclusions: The present method is robust, stable, applicable with an experimentally realistic amount of data, and does not require any prior knowledge of the studied network. Therefore, it can be used on a personal computer as a turn-key procedure to infer connectivity graphs and generate simulation models from simultaneous spike train recordings
dc.relation.isversionofjnlnameJournal of Neuroscience Methods
dc.relation.isversionofjnlvol297
dc.relation.isversionofjnlissue1 March 2018
dc.relation.isversionofjnldate2018
dc.relation.isversionofjnlpages9-21
dc.relation.isversionofdoi10.1016/j.jneumeth.2017.12.026
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-01585986
dc.subject.ddclabelSciences connexes (physique, astrophysique)en
dc.description.ssrncandidatenon
dc.description.halcandidatenon
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2018-07-18T14:39:31Z
hal.person.labIds542136*
hal.person.labIds199970*
hal.person.labIds542136*
hal.person.labIds60*
hal.person.labIds268400*
hal.person.labIds542136*
hal.person.labIds199970*


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