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Synaptic scaling in combination with many generic plasticity mechanisms stabilizes circuit connectivity

dc.contributor.authorTetzlaff, Christian
dc.contributor.authorKolodziejski, C.
dc.contributor.authorTimme, M.
dc.contributor.authorWörgötter, F.
dc.date.accessioned2012-07-16T11:22:42Z
dc.date.available2012-07-16T11:22:42Z
dc.date.issued2011
dc.identifier.citationTetzlaff, Christian (2011): Synaptic scaling in combination with many generic plasticity mechanisms stabilizes circuit connectivity - Frontiers in Computational Neuroscience, Vol. 5, Nr. , p. -
dc.relation.ISSN1662-5188
dc.identifier.urihttp://resolver.sub.uni-goettingen.de/purl?gs-1/7779
dc.description.abstractSynaptic scaling is a slow process that modifies synapses, keeping the firing rate of neural circuits in specific regimes. Together with other processes, such as conventional synaptic plasticity in the form of long term depression and potentiation, synaptic scaling changes the synaptic patterns in a network, ensuring diverse, functionally relevant, stable, and input-dependent connectivity. How synaptic patterns are generated and stabilized, however, is largely unknown. Here we formally describe and analyze synaptic scaling based on results from experimental studies and demonstrate that the combination of different conventional plasticity mechanisms and synaptic scaling provides a powerful general framework for regulating network connectivity. In addition, we design several simple models that reproduce experimentally observed synaptic distributions as well as the observed synaptic modifications during sustained activity changes. These models predict that the combination of plasticity with scaling generates globally stable, input-controlled synaptic patterns, also in recurrent networks. Thus, in combination with other forms of plasticity, synaptic scaling can robustly yield neuronal circuits with high synaptic diversity, which potentially enables robust dynamic storage of complex activation patterns. This mechanism is even more pronounced when considering networks with a realistic degree of inhibition. Synaptic scaling combined with plasticity could thus be the basis for learning structured behavior even in initially random networks.
dc.language.isoeng
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/270273/EU//Xperience
dc.rightsopenAccess
dc.subjectplasticity; neuralnetwork; homeostasis; synapse
dc.titleSynaptic scaling in combination with many generic plasticity mechanisms stabilizes circuit connectivity
dc.typejournalArticle
dc.identifier.doi10.3389/fncom.2011.00047
dc.type.versionpublishedVersion
dc.identifier.fs585625
dc.bibliographicCitation.volume5
dc.bibliographicCitation.firstPage1
dc.bibliographicCitation.lastPage14
dc.type.subtypejournalArticle
dc.identifier.pmid22203799
dc.relation.euprojectXPERIENCE
dc.description.statuspeerReviewed
dc.bibliographicCitation.journalFrontiers in Computational Neuroscience


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