dc.contributor.author | Steuber, Volker | |
dc.contributor.author | De Schutter, E. | |
dc.date.accessioned | 2011-10-06T08:01:15Z | |
dc.date.available | 2011-10-06T08:01:15Z | |
dc.date.issued | 2001-06 | |
dc.identifier.citation | Steuber , 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.issn | 0925-2312 | |
dc.identifier.other | ORCID: /0000-0003-0186-3580/work/133139280 | |
dc.identifier.uri | http://hdl.handle.net/2299/6591 | |
dc.description | Full text of this article is not available in the UHRA | |
dc.description.abstract | It 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.extent | 6 | |
dc.language.iso | eng | |
dc.relation.ispartof | Neurocomputing | |
dc.subject | cerebellum Purkinje cells | |
dc.subject | LTD | |
dc.subject | pattern recognition | |
dc.subject | learning | |
dc.subject | ACTIVE MEMBRANE MODEL | |
dc.subject | SIMULATION | |
dc.subject | RESPONSES | |
dc.title | Long-term depression and recognition of parallel fibre patterns in a multi-compartmental model of a cerebellar Purkinje cell | en |
dc.contributor.institution | Centre for Computer Science and Informatics Research | |
dc.contributor.institution | Department of Computer Science | |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | Centre of Data Innovation Research | |
dc.contributor.institution | Centre for Future Societies Research | |
dc.contributor.institution | Biocomputation Research Group | |
dc.description.status | Peer reviewed | |
rioxxterms.versionofrecord | 10.1016/S0925-2312(01)00458-1 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |