A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function
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Data
2008-01-01
Autores
Simões, Alexandre da Silva [UNESP]
Reali Costa, Anna Helena
Zaverucha, G
LoureiroDaCosta, A
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Springer-verlag Berlin
Resumo
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.
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Advances In Artificial Intelligence - Sbia 2008, Proceedings. Berlin: Springer-verlag Berlin, v. 5249, p. 227-236, 2008.