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dc.contributor.authorRule, Michael E
dc.contributor.authorO'Leary, Timothy
dc.date.accessioned2022-03-15T02:04:14Z
dc.date.available2022-03-15T02:04:14Z
dc.date.issued2022-02-15
dc.identifier.issn0027-8424
dc.identifier.otherPMC8851551
dc.identifier.other35145024
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/334994
dc.description.abstractAs an adaptive system, the brain must retain a faithful representation of the world while continuously integrating new information. Recent experiments have measured population activity in cortical and hippocampal circuits over many days and found that patterns of neural activity associated with fixed behavioral variables and percepts change dramatically over time. Such "representational drift" raises the question of how malleable population codes can interact coherently with stable long-term representations that are found in other circuits and with relatively rigid topographic mappings of peripheral sensory and motor signals. We explore how known plasticity mechanisms can allow single neurons to reliably read out an evolving population code without external error feedback. We find that interactions between Hebbian learning and single-cell homeostasis can exploit redundancy in a distributed population code to compensate for gradual changes in tuning. Recurrent feedback of partially stabilized readouts could allow a pool of readout cells to further correct inconsistencies introduced by representational drift. This shows how relatively simple, known mechanisms can stabilize neural tuning in the short term and provides a plausible explanation for how plastic neural codes remain integrated with consolidated, long-term representations.
dc.languageeng
dc.publisherProceedings of the National Academy of Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourcenlmid: 7505876
dc.sourceessn: 1091-6490
dc.subjectHomeostasis
dc.subjectLifelong Learning
dc.subjectHebbian Plasticity
dc.subjectRepresentational Drift
dc.subjectNerve Net
dc.subjectNeurons
dc.subjectAnimals
dc.subjectNeuronal Plasticity
dc.subjectModels, Neurological
dc.titleSelf-healing codes: How stable neural populations can track continually reconfiguring neural representations.
dc.typeArticle
dc.date.updated2022-03-15T02:04:14Z
prism.issueIdentifier7
prism.publicationNameProc Natl Acad Sci U S A
prism.volume119
dc.identifier.doi10.17863/CAM.82432
dcterms.dateAccepted2021-12-29
rioxxterms.versionofrecord10.1073/pnas.2106692119
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.contributor.orcidRule, Michael E [0000-0002-4196-774X]
dc.contributor.orcidO'Leary, Timothy [0000-0002-1029-0158]
dc.identifier.eissn1091-6490
pubs.funder-project-idEuropean Research Council (716643)
cam.issuedOnline2022-02-10


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