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dc.contributor.authorSimões, Alexandre da Silva [UNESP]
dc.contributor.authorReali Costa, Anna Helena
dc.contributor.authorZaverucha, G
dc.contributor.authorLoureiroDaCosta, A
dc.date.accessioned2014-05-20T13:12:14Z
dc.date.available2014-05-20T13:12:14Z
dc.date.issued2008-01-01
dc.identifierhttp://dx.doi.org/10.1007/978-3-540-88190-2_28
dc.identifier.citationAdvances In Artificial Intelligence - Sbia 2008, Proceedings. Berlin: Springer-verlag Berlin, v. 5249, p. 227-236, 2008.
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11449/214
dc.description.abstractSpiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.en
dc.format.extent227-236
dc.language.isoeng
dc.publisherSpringer-verlag Berlin
dc.relation.ispartofAdvances In Artificial Intelligence - Sbia 2008, Proceedings
dc.sourceWeb of Science
dc.titleA Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Functionen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dcterms.rightsHolderSpringer-verlag Berlin
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationSão Paulo State Univ UNESP, Automat & Integrated Syst Grp, BR-18087180 Sorocaba, SP, Brazil
dc.description.affiliationUnespSão Paulo State Univ UNESP, Automat & Integrated Syst Grp, BR-18087180 Sorocaba, SP, Brazil
dc.identifier.doi10.1007/978-3-540-88190-2_28
dc.identifier.wosWOS:000261373200028
dc.rights.accessRightsAcesso aberto
dc.identifier.scopus2-s2.0-57049154145
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Ciência e Tecnologia, Sorocabapt
unesp.author.lattes1368002066043197[1]
unesp.author.orcid0000-0001-7309-4528[2]
unesp.author.orcid0000-0002-1457-6305[1]
dc.relation.ispartofsjr0,295
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