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dc.contributor.authorKoutsou, Achilleasen
dc.contributor.authorChristodoulou, Chris C.en
dc.contributor.authorBugmann, G.en
dc.contributor.authorKanev, J.en
dc.creatorKoutsou, Achilleasen
dc.creatorChristodoulou, Chris C.en
dc.creatorBugmann, G.en
dc.creatorKanev, J.en
dc.date.accessioned2019-11-13T10:40:47Z
dc.date.available2019-11-13T10:40:47Z
dc.date.issued2012
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/54297
dc.description.abstractIn this letter, we aim to measure the relative contribution of coincidence detection and temporal integration to the firing of spikes of a simple neuron model. To this end, we develop a method to infer the degree of synchrony in an ensemble of neurons whose firing drives a single postsynaptic cell. This is accomplished by studying the effects of synchronous inputs on the membrane potential slope of the neuron and estimating the degree of response-relevant input synchrony, which determines the neuron's operational mode. The measure is calculated using the normalized slope of the membrane potential prior to the spikes fired by a neuron, and we demonstrate that it is able to distinguish between the two operational modes. By applying this measure to the membrane potential time course of a leaky integrate-and-fire neuron with the partial somatic reset mechanism, which has been shown to be the most likely candidate to reflect the mechanism used in the brain for reproducing the highly irregular firing at high rates, we show that the partial reset model operates as a temporal integrator of incoming excitatory postsynaptic potentials and that coincidence detection is not necessary for producing such high irregular firing. © 2012 Massachusetts Institute of Technology.en
dc.sourceNeural computationen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84871902872&doi=10.1162%2fNECO_a_00323&partnerID=40&md5=11c3578fbe2bd82f58635456666a71ca
dc.subjectarticleen
dc.subjecthumanen
dc.subjectHumansen
dc.subjectTime Factorsen
dc.subjectbiological modelen
dc.subjectAnimalsen
dc.subjectanimalen
dc.subjectphysiologyen
dc.subjecttimeen
dc.subjectcomputer simulationen
dc.subjectNeuronsen
dc.subjectartificial neural networken
dc.subjectNeural Networks (Computer)en
dc.subjectnerve cellen
dc.subjectMembrane Potentialsen
dc.subjectmathematicsen
dc.subjectModels, Neurologicalen
dc.subjectmembrane potentialen
dc.titleDistinguishing the causes of firing with themembrane potential slopeen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1162/NECO_a_00323
dc.description.volume24
dc.description.issue9
dc.description.startingpage2318
dc.description.endingpage2345
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :3</p>en
dc.source.abbreviationNeural Comp.en
dc.contributor.orcidChristodoulou, Chris C. [0000-0001-9398-5256]
dc.gnosis.orcid0000-0001-9398-5256


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