dc.contributor | Universidade Estadual Paulista (Unesp) | |
dc.date.accessioned | 2014-05-20T13:12:14Z | |
dc.date.available | 2014-05-20T13:12:14Z | |
dc.date.created | 2014-05-20T13:12:14Z | |
dc.date.issued | 2008-01-01 | |
dc.identifier | Advances In Artificial Intelligence - Sbia 2008, Proceedings. Berlin: Springer-verlag Berlin, v. 5249, p. 227-236, 2008. | |
dc.identifier | 0302-9743 | |
dc.identifier | http://hdl.handle.net/11449/214 | |
dc.identifier | 10.1007/978-3-540-88190-2_28 | |
dc.identifier | WOS:000261373200028 | |
dc.identifier | 2-s2.0-57049154145 | |
dc.description.abstract | Spiking 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. | |
dc.language | eng | |
dc.publisher | Springer-verlag Berlin | |
dc.relation | Advances In Artificial Intelligence - Sbia 2008, Proceedings | |
dc.relation | 0,295 | |
dc.rights | Acesso aberto | |
dc.source | Web of Science | |
dc.title | A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function | |
dc.type | Actas de congresos | |