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dc.contributor.authorDe Sousa, Giseli
dc.contributor.authorMaex, Reinoud
dc.contributor.authorAdams, R.G.
dc.contributor.authorDavey, N.
dc.contributor.authorSteuber, Volker
dc.date.accessioned2015-04-23T14:34:00Z
dc.date.available2015-04-23T14:34:00Z
dc.date.issued2015-04-01
dc.identifier.citationDe Sousa , G , Maex , R , Adams , R G , Davey , N & Steuber , V 2015 , ' Dendritic Morphology Predicts Pattern Recognition Performance in Multi-compartmental Model Neurons with and without Active Conductances ' , Journal of Computational Neuroscience , vol. 38 , no. 2 , pp. 221-234 . https://doi.org/10.1007/s10827-014-0537-1
dc.identifier.issn0929-5313
dc.identifier.otherPURE: 1372942
dc.identifier.otherPURE UUID: fd5ef6ec-d6d1-477e-a59d-d5eaebc89000
dc.identifier.otherScopus: 84925488819
dc.identifier.urihttp://hdl.handle.net/2299/15820
dc.descriptionThis is an Open Access article published under the Creative Commons Attribution license CC BY 4.0 which allows users to read, copy, distribute and make derivative works, as long as the author of the original work is cited
dc.description.abstractIn this paper we examine how a neuron’s dendritic morphology can affect its pattern recognition performance. We use two different algorithms to systematically explore the space of dendritic morphologies: an algorithm that generates all possible dendritic trees with 22 terminal points, and one that creates representative samples of trees with 128 terminal points. Based on these trees, we construct multi-compartmental models. To assess the performance of the resulting neuronal models, we quantify their ability to discriminate learnt and novel input patterns. We find that the dendritic morphology does have a considerable effect on pattern recognition performance and that the neuronal performance is inversely correlated with the mean depth of the dendritic tree. The results also reveal that the asymmetry index of the dendritic tree does not correlate with the performance for the full range of tree morphologies. The performance of neurons with dendritic tapering is best predicted by the mean and variance of the electrotonic distance of their synapses to the soma. All relationships found for passive neuron models also hold, even in more accentuated form, for neurons with active membranesen
dc.language.isoeng
dc.relation.ispartofJournal of Computational Neuroscience
dc.rightsOpen
dc.titleDendritic Morphology Predicts Pattern Recognition Performance in Multi-compartmental Model Neurons with and without Active Conductancesen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Physics, Engineering & Computer Science
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2015-04-01
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1007/s10827-014-0537-1
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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