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dc.contributor.authorSteuber, Volker
dc.contributor.authorDe Schutter, E.
dc.date.accessioned2011-10-06T08:01:15Z
dc.date.available2011-10-06T08:01:15Z
dc.date.issued2001-06
dc.identifier.citationSteuber , V & De Schutter , E 2001 , ' Long-term depression and recognition of parallel fibre patterns in a multi-compartmental model of a cerebellar Purkinje cell ' , Neurocomputing , vol. 38-40 , pp. 383-388 . https://doi.org/10.1016/S0925-2312(01)00458-1
dc.identifier.issn0925-2312
dc.identifier.otherORCID: /0000-0003-0186-3580/work/133139280
dc.identifier.urihttp://hdl.handle.net/2299/6591
dc.descriptionFull text of this article is not available in the UHRA
dc.description.abstractIt has been suggested that long-term depression (LTD) of parallel fibre (PF) synapses enables a cerebellar Purkinje cell (PC) to learn to recognise PF activity patterns. We investigate the recognition of PF patterns that have been stored by LTD of AMPA receptors in a multi-compartmental PC model with a passive soma. We iind that a corresponding artificial neural network outperforms a PC model with active dendrites by an order of magnitude. Removal of the dendritic ion channels leads to a further decrease in performance. Another effect of the active dendrites is an afterhyperpolarization response to novel PF patterns. Thus, the LTD based storage of PF patterns can lead to a potentiated late PC response. (C) 2001 Elsevier Science B.V. All rights reserved.en
dc.format.extent6
dc.language.isoeng
dc.relation.ispartofNeurocomputing
dc.subjectcerebellum Purkinje cells
dc.subjectLTD
dc.subjectpattern recognition
dc.subjectlearning
dc.subjectACTIVE MEMBRANE MODEL
dc.subjectSIMULATION
dc.subjectRESPONSES
dc.titleLong-term depression and recognition of parallel fibre patterns in a multi-compartmental model of a cerebellar Purkinje cellen
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionCentre of Data Innovation Research
dc.contributor.institutionCentre for Future Societies Research
dc.description.statusPeer reviewed
rioxxterms.versionofrecord10.1016/S0925-2312(01)00458-1
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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