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Synaptic depression and slow oscillatory activity in a biophysical network model of the cerebral cortex

dc.contributor.authorBenita, Jose M.
dc.contributor.authorGuillamon Grabolosa, Antoni
dc.contributor.authorDeco, Gustavo
dc.contributor.authorSánchez-Vives, Maria V.
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtica Aplicada I
dc.date.accessioned2012-10-15T10:26:44Z
dc.date.available2012-10-15T10:26:44Z
dc.date.created2012-08-28
dc.date.issued2012-08-28
dc.identifier.citationBenita, J. [et al.]. Synaptic depression and slow oscillatory activity in a biophysical network model of the cerebral cortex. "Frontiers in Computational Neuroscience", 28 Agost 2012, vol. 6, núm. 64, p. 1-17.
dc.identifier.issn1662-5188
dc.identifier.urihttp://hdl.handle.net/2117/16721
dc.description.abstractShort-termsynapticdepression(STD)isaformofsynapticplasticitythathasalargeimpactonnetworkcomputations.ExperimentalresultssuggestthatSTDismodulatedbycorticalactivity,decreasingwithactivityinthenetworkandincreasingduringsilentstates.Here,weexploreddifferentactivity-modulationprotocolsinabiophysicalnetworkmodelforwhichthemodeldisplayedlessSTDwhenthenetworkwasactivethanwhenitwassilent,inagreementwithexperimentalresults.Furthermore,westudiedhowtrainsofsynapticpotentialshadlesserdecayduringperiodsofactivity(UPstates)thanduringsilentperiods(DOWNstates),providingnewexperimentalpredictions.WenexttackledtheinversequestionofwhatistheimpactofmodifyingSTDparametersontheemergentactivityofthenetwork,aquestiondifficulttoanswerexperimentally.Wefoundthatsynapticdepressionofcorticalconnectionshadacriticalroletodeterminetheregimeofrhythmiccorticalactivity.WhilelowSTDresultedinanemergentrhythmicactivitywithshortUPstatesandlongDOWNstates,increasingSTDresultedinlongerandmorefrequentUPstatesinterleavedwithshortsilentperiods.Astillhighersynapticdepressionsetthenetworkintoanon-oscillatoryfiringregimewhereDOWNstatesnolongeroccurred.ThespeedofpropagationofUPstatesalongthenetworkwasnotfoundtobemodulatedbySTDduringtheoscillatoryregime;itremainedrelativelystableoverarangeofvaluesofSTD.Overall,wefoundthatthemutualinteractionsbetweensynapticdepressionandongoingnetworkactivityarecriticaltodeterminethemechanismsthatmodulatecorticalemergentpatterns
dc.format.extent17 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Matemàtica aplicada a les ciències
dc.subject.lcshCerebral cortex
dc.subject.lcshComputational neuroscience
dc.subject.othercortical activity
dc.subject.othernetwork model
dc.subject.othershort-term depression
dc.subject.otherup/down state
dc.subject.othersynaptic plasticity
dc.titleSynaptic depression and slow oscillatory activity in a biophysical network model of the cerebral cortex
dc.typeArticle
dc.subject.lemacEscorça cerebral
dc.subject.lemacNeurociència computacional
dc.contributor.groupUniversitat Politècnica de Catalunya. EGSA - Equacions Diferencials, Geometria, Sistemes Dinàmics i de Control, i Aplicacions
dc.identifier.doi10.3389/fncom.2012.00064
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3428579/
dc.rights.accessOpen Access
local.identifier.drac10977076
dc.description.versionPostprint (published version)
local.citation.authorBenita, J.; Guillamon, A.; Deco, G.; Sánchez-Vives, M.
local.citation.publicationNameFrontiers in Computational Neuroscience
local.citation.volume6
local.citation.number64
local.citation.startingPage1
local.citation.endingPage17
dc.identifier.pmid22973221


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