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dc.contributor.authorOrbán, Gen
dc.contributor.authorBerkes, Pen
dc.contributor.authorFiser, Jen
dc.contributor.authorLengyel, Mateen
dc.date.accessioned2016-11-25T10:56:08Z
dc.date.available2016-11-25T10:56:08Z
dc.date.issued2016-10-19en
dc.identifier.issn0896-6273
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/261301
dc.description.abstractNeural responses in the visual cortex are variable, and there is now an abundance of data characterizing how the magnitude and structure of this variability depends on the stimulus. Current theories of cortical computation fail to account for these data; they either ignore variability altogether or only model its unstructured Poisson-like aspects. We develop a theory in which the cortex performs probabilistic inference such that population activity patterns represent statistical samples from the inferred probability distribution. Our main prediction is that perceptual uncertainty is directly encoded by the variability, rather than the average, of cortical responses. Through direct comparisons to previously published data as well as original data analyses, we show that a sampling-based probabilistic representation accounts for the structure of noise, signal, and spontaneous response variability and correlations in the primary visual cortex. These results suggest a novel role for neural variability in cortical dynamics and computations.
dc.description.sponsorshipEuropean Union-FP7 (Marie Curie Intra-European Fellowship, Marie Curie CIG), Hungarian Academy of Sciences (Lendulet Award), Swartz Foundation, Swiss National Science Foundation, National Science Foundation, Wellcome Trust
dc.languageENGen
dc.language.isoenen
dc.publisherElsevier
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectBayesian computationsen
dc.subjectV1en
dc.subjectnatural imagesen
dc.subjectnoise correlationsen
dc.subjectnormative modelen
dc.subjectspontaneous activityen
dc.subjectstochastic samplingen
dc.subjecttheoryen
dc.subjectvariabilityen
dc.subjectvisionen
dc.titleNeural Variability and Sampling-Based Probabilistic Representations in the Visual Cortexen
dc.typeArticle
prism.endingPage543
prism.issueIdentifier2en
prism.publicationDate2016en
prism.publicationNameNeuronen
prism.startingPage530
prism.volume92en
dc.identifier.doi10.17863/CAM.6474
dcterms.dateAccepted2016-09-06en
rioxxterms.versionofrecord10.1016/j.neuron.2016.09.038en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2016-10-19en
dc.contributor.orcidLengyel, Mate [0000-0001-7266-0049]
dc.identifier.eissn1097-4199
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idWellcome Trust (095621/Z/11/Z)
cam.orpheus.successThu Jan 30 12:56:56 GMT 2020 - The item has an open VoR version.*
rioxxterms.freetoread.startdate2100-01-01


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International