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dc.contributor.authorHennequin, Guillaume
dc.contributor.authorAhmadian, Yashar
dc.contributor.authorRubin, Daniel B
dc.contributor.authorLengyel, Máté
dc.contributor.authorMiller, Kenneth D
dc.date.accessioned2018-10-10T10:44:22Z
dc.date.available2018-10-10T10:44:22Z
dc.date.issued2018-05-16
dc.identifier.issn0896-6273
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/283508
dc.description.abstractCorrelated variability in cortical activity is ubiquitously quenched following stimulus onset, in a stimulus-dependent manner. These modulations have been attributed to circuit dynamics involving either multiple stable states ("attractors") or chaotic activity. Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic "stabilized supralinear network"), best explains these modulations. Given the supralinear input/output functions of cortical neurons, increased stimulus drive strengthens effective network connectivity. This shifts the balance from interactions that amplify variability to suppressive inhibitory feedback, quenching correlated variability around more strongly driven steady states. Comparing to previously published and original data analyses, we show that this mechanism, unlike previous proposals, uniquely accounts for the spatial patterns and fast temporal dynamics of variability suppression. Specifying the cortical operating regime is key to understanding the computations underlying perception.
dc.format.mediumPrint
dc.languageeng
dc.publisherElsevier BV
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectOccipital Lobe
dc.subjectVisual Cortex
dc.subjectNeurons
dc.subjectAnimals
dc.subjectMacaca
dc.subjectNeural Inhibition
dc.subjectNonlinear Dynamics
dc.subjectNeural Networks, Computer
dc.titleThe Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability.
dc.typeArticle
prism.endingPage860.e5
prism.issueIdentifier4
prism.publicationDate2018
prism.publicationNameNeuron
prism.startingPage846
prism.volume98
dc.identifier.doi10.17863/CAM.30871
dcterms.dateAccepted2018-04-12
rioxxterms.versionofrecord10.1016/j.neuron.2018.04.017
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-05
dc.contributor.orcidHennequin, Guillaume [0000-0002-7296-6870]
dc.contributor.orcidLengyel, Mate [0000-0001-7266-0049]
dc.identifier.eissn1097-4199
rioxxterms.typeJournal Article/Review
pubs.funder-project-idWellcome Trust (202111/Z/16/Z)
pubs.funder-project-idWellcome Trust (095621/Z/11/Z)


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