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dc.contributor.authorOrosz, Gáboren_GB
dc.contributor.authorAshwin, Peteren_GB
dc.contributor.authorTownley, Stuarten_GB
dc.date.accessioned2010-01-08T17:00:42Zen_GB
dc.date.accessioned2011-01-25T10:33:16Zen_GB
dc.date.accessioned2013-03-20T12:29:48Z
dc.date.issued2009en_GB
dc.description.abstractIn this paper, we consider a learning strategy that allows one to transmit information between two coupled phase oscillator systems (called teaching and learning systems) via frequency adaptation. The dynamics of these systems can be modelled with reference to a number of partially synchronized cluster states and transitions between them. Forcing the teaching system by steady but spatially nonhomogeneous inputs produces cyclic sequences of transitions between the cluster states, that is, information about inputs is encoded via a “winnerless competition” process into spatio–temporal codes. The large variety of codes can be learned by the learning system that adapts its frequencies to those of the teaching system. We visualize the dynamics using “weighted order parameters (WOPs)” that are analogous to “local field potentials” in neural systems. Since spatio–temporal coding is a mechanism that appears in olfactory systems, the developed learning rules may help to extract information from these neural ensembles.en_GB
dc.identifier.citation20 (7), pp. 1135-1147en_GB
dc.identifier.doi10.1109/TNN.2009.2016658en_GB
dc.identifier.urihttp://hdl.handle.net/10036/89053en_GB
dc.language.isoenen_GB
dc.publisherIEEEen_GB
dc.relation.urlhttp://dx.doi.org/10.1109/TNN.2009.2016658en_GB
dc.subjectadaptive learningen_GB
dc.subjectcoupled oscillator systemen_GB
dc.subjectheteroclinic networken_GB
dc.subjectspatio–temporal codeen_GB
dc.subjectwinnerless competitionen_GB
dc.titleLearning of spatio–temporal codes in a coupled oscillator systemen_GB
dc.typeArticleen_GB
dc.date.available2010-01-08T17:00:42Zen_GB
dc.date.available2011-01-25T10:33:16Zen_GB
dc.date.available2013-03-20T12:29:48Z
dc.identifier.issn1045-9227en_GB
dc.identifier.issn1941-0093en_GB
dc.description©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_GB
dc.identifier.journalIEEE Transactions on Neural Networksen_GB


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