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dc.contributor.authorSpreemann, Gard
dc.contributor.authorDunn, Benjamin Adric
dc.contributor.authorBotnan, Magnus
dc.contributor.authorBaas, Nils A.
dc.date.accessioned2019-04-01T11:49:57Z
dc.date.available2019-04-01T11:49:57Z
dc.date.created2018-08-16T15:59:48Z
dc.date.issued2018
dc.identifier.citationPhysical review. E. 2018, 97 (3), 1-14.nb_NO
dc.identifier.issn2470-0045
dc.identifier.urihttp://hdl.handle.net/11250/2592704
dc.description.abstractWe propose a method, based on persistent homology, to uncover topological properties of a priori unknown covariates in a system governed by the kinetic Ising model with time-varying external fields. As its starting point the method takes observations of the system under study, a list of suspected or known covariates, and observations of those covariates. We infer away the contributions of the suspected or known covariates, after which persistent homology reveals topological information about unknown remaining covariates. Our motivating example system is the activity of neurons tuned to the covariates physical position and head direction, but the method is far more general.nb_NO
dc.language.isoengnb_NO
dc.publisherAmerican Physical Societynb_NO
dc.titleUsing persistent homology to reveal hidden covariates in systems governed by the kinetic Ising modelnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber1-14nb_NO
dc.source.volume97nb_NO
dc.source.journalPhysical review. Enb_NO
dc.source.issue3nb_NO
dc.identifier.doi10.1103/PhysRevE.97.032313
dc.identifier.cristin1602484
dc.description.localcode©2018 American Physical Society. This article is available at: https://doi.org/10.1103/PhysRevE.97.032313nb_NO
cristin.unitcode194,63,15,0
cristin.unitcode194,65,60,0
cristin.unitnameInstitutt for matematiske fag
cristin.unitnameKavliinstitutt for nevrovitenskap
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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