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.
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References
Hopfield, J.: Pattern recognition computation using action potential timing for stimulus representation. Nature 376, 33–36 (1995)
Gerstner, W., Kempter, R., Van Hemmen, J.L., Wagner, H.: A neuronal learning rule for sub-millisecond temporal coding. Nature (384), 76–78 (September 1996)
Bohte, S.M.: Spiking Neural Networks. PhD thesis, Institute for Programming Research and Algorithmics. Centre for Mathematics and Computer Science, Amsterdam (2003)
Natschläger, T., Ruf, B.: Spatial and temporal pattern analysis via spiking neurons. Network 9(3), 319–332 (1998)
Maass, W., Bishop, C.M. (eds.): Pulsed Neural Networks. MIT Press, Cambridge (1999)
da Simöes, A.S.: Unsupervised learning in spiking neural networks with radial basis functions. PhD thesis, Escola Politécnica da Universidade de Säo Paulo (2006)
Prechelt, L.: PROBEN1 – A set of neural network benchmark problems and benchmarking rules. Technical Report 21/94, Technical Report 21/94. University of Karlsruhe, Germany (September 1994)
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da Silva Simões, A., Costa, A.H.R. (2008). A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function. In: Zaverucha, G., da Costa, A.L. (eds) Advances in Artificial Intelligence - SBIA 2008. SBIA 2008. Lecture Notes in Computer Science(), vol 5249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88190-2_28
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DOI: https://doi.org/10.1007/978-3-540-88190-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88189-6
Online ISBN: 978-3-540-88190-2
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