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dc.contributor.authorRybakken, Erik
dc.contributor.authorBaas, Nils A.
dc.contributor.authorDunn, Benjamin Adric
dc.date.accessioned2020-01-13T12:28:20Z
dc.date.available2020-01-13T12:28:20Z
dc.date.created2019-01-08T09:03:53Z
dc.date.issued2019
dc.identifier.citationNeural Computation. 2019, 31 (1), 68-93.nb_NO
dc.identifier.issn0899-7667
dc.identifier.urihttp://hdl.handle.net/11250/2635974
dc.description.abstractWe introduce a novel data-driven approach to discover and decode features in the neural code coming from large population neural recordings with minimal assumptions, using cohomological feature extraction. We apply our approach to neural recordings of mice moving freely in a box, where we find a circular feature. We then observe that the decoded value corresponds well to the head direction of the mouse. Thus, we capture head direction cells and decode the head direction from the neural population activity without having to process the mouse's behavior. Interestingly, the decoded values convey more information about the neural activity than the tracked head direction does, with differences that have some spatial organization. Finally, we note that the residual population activity, after the head direction has been accounted for, retains some low-dimensional structure that is correlated with the speed of the mouse.nb_NO
dc.language.isoengnb_NO
dc.publisherMassachusetts Institute of Technology Press (MIT Press)nb_NO
dc.titleDecoding of neural data using cohomological feature extractionnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber68-93nb_NO
dc.source.volume31nb_NO
dc.source.journalNeural Computationnb_NO
dc.source.issue1nb_NO
dc.identifier.doi10.1162/neco_a_01150
dc.identifier.cristin1651966
dc.description.localcode© 2018 Massachusetts Institute of Technology.nb_NO
cristin.unitcode194,63,15,0
cristin.unitnameInstitutt for matematiske fag
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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