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dc.contributor.authorEcheveste, Rodrigo
dc.contributor.authorLengyel, Mate
dc.date.accessioned2018-11-17T00:31:26Z
dc.date.available2018-11-17T00:31:26Z
dc.date.issued2018-11
dc.identifier.issn0166-2236
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/285367
dc.description.abstractIn 2006, Ma et al. (Nat. Neurosci. 1006;9:1432-1438) presented an elegant theory for how populations of neurons might represent uncertainty to perform Bayesian inference. Critically, according to this theory, neural variability is no longer a nuisance, but rather a vital part of how the brain encodes probability distributions and performs computations with them.
dc.description.sponsorshipERC Consolidator Grant (726090-COGTOM)
dc.format.mediumPrint
dc.languageeng
dc.publisherElsevier BV
dc.subjectBrain
dc.subjectNerve Net
dc.subjectNeurons
dc.subjectAnimals
dc.subjectHumans
dc.subjectProbability
dc.subjectBayes Theorem
dc.subjectModels, Neurological
dc.titleThe Redemption of Noise: Inference with Neural Populations.
dc.typeArticle
prism.endingPage770
prism.issueIdentifier11
prism.publicationDate2018
prism.publicationNameTrends Neurosci
prism.startingPage767
prism.volume41
dc.identifier.doi10.17863/CAM.27620
dcterms.dateAccepted2018-09-07
rioxxterms.versionofrecord10.1016/j.tins.2018.09.003
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-11
dc.contributor.orcidEcheveste, Rodrigo [0000-0002-6155-8679]
dc.contributor.orcidLengyel, Mate [0000-0001-7266-0049]
dc.identifier.eissn1878-108X
rioxxterms.typeJournal Article/Review
pubs.funder-project-idWellcome Trust (095621/Z/11/Z)
rioxxterms.freetoread.startdate2019-11-30


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