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dc.contributor.authorTrapp, Philip
dc.contributor.authorEcheveste, Rodrigo
dc.contributor.authorGros, Claudius
dc.date.accessioned2018-11-07T00:30:19Z
dc.date.available2018-11-07T00:30:19Z
dc.date.issued2018-06-12
dc.identifier.issn2045-2322
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/284675
dc.description.abstractSpontaneous brain activity is characterized in part by a balanced asynchronous chaotic state. Cortical recordings show that excitatory (E) and inhibitory (I) drivings in the E-I balanced state are substantially larger than the overall input. We show that such a state arises naturally in fully adapting networks which are deterministic, autonomously active and not subject to stochastic external or internal drivings. Temporary imbalances between excitatory and inhibitory inputs lead to large but short-lived activity bursts that stabilize irregular dynamics. We simulate autonomous networks of rate-encoding neurons for which all synaptic weights are plastic and subject to a Hebbian plasticity rule, the flux rule, that can be derived from the stationarity principle of statistical learning. Moreover, the average firing rate is regulated individually via a standard homeostatic adaption of the bias of each neuron's input-output non-linear function. Additionally, networks with and without short-term plasticity are considered. E-I balance may arise only when the mean excitatory and inhibitory weights are themselves balanced, modulo the overall activity level. We show that synaptic weight balance, which has been considered hitherto as given, naturally arises in autonomous neural networks when the here considered self-limiting Hebbian synaptic plasticity rule is continuously active.
dc.format.mediumElectronic
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleE-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks.
dc.typeArticle
prism.issueIdentifier1
prism.publicationDate2018
prism.publicationNameSci Rep
prism.startingPage8939
prism.volume8
dc.identifier.doi10.17863/CAM.32048
dcterms.dateAccepted2018-05-24
rioxxterms.versionofrecord10.1038/s41598-018-27099-5
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-06-12
dc.contributor.orcidEcheveste, Rodrigo [0000-0002-6155-8679]
dc.identifier.eissn2045-2322
rioxxterms.typeJournal Article/Review
cam.issuedOnline2018-06-12


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