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dc.contributor.authorTheofilou, D.
dc.contributor.authorSteuber, Volker
dc.contributor.authorDe Schutter, E.
dc.identifier.citationTheofilou , D , Steuber , V & De Schutter , E 2003 , ' Novelty detection in a Kohonen-like network with a long-term depression learning rule ' , Neurocomputing , vol. 52-4 , pp. 411-417 .
dc.identifier.otherPURE: 381279
dc.identifier.otherPURE UUID: 5f3a5b63-4480-4f79-bb39-acd30059ed58
dc.identifier.otherWOS: 000183514300060
dc.identifier.otherScopus: 0037781033
dc.descriptionFull text of this article is not available in the UHRA
dc.description.abstractIn the cerebellar cortex, long-term depression (LTD) of synapses between parallel fibers (PF) and Purkinje neurons can spread to neighboring ones, independently of their activation by PF input. This spread of non-specific LTD around the activated synapses resembles how units are affected in the neighborhood of the winner in a Kohonen Network (KN). However in a classic KN the weight vectors become more similar to the input vector with learning, while in the LTD case they should become more dissimilar. We devised a new LTD-KN where units, opposite to the classic KN, decrease their response (LTD-like) when a pattern is learned and we show that this LTD-KN functions as a novelty detector. (C) 2002 Elsevier Science B.V. All rights reserved.en
dc.subjectlong-term depression
dc.subjectself-organizing map
dc.subjectKohonen network
dc.subjectnovelty detector
dc.titleNovelty detection in a Kohonen-like network with a long-term depression learning ruleen
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.typeJournal Article/Review

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