dc.contributor.author Mohemmed, A
dc.contributor.author Schliebs, S
dc.contributor.author Matsuda, S
dc.contributor.author Kasabov, N
dc.contributor.editor Hojjat Adeli
dc.date.accessioned 2012-10-01T04:34:20Z
dc.date.available 2012-10-01T04:34:20Z
dc.date.copyright 2012
dc.date.issued 2012-10-01
dc.identifier.citation International Journal of Neural Systems, Vol. 22, No. 4 (2012) 1250012 (17 pages)
dc.identifier.uri http://hdl.handle.net/10292/4619
dc.description.abstract Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-temporal information. However, due to their inherent complexity, the formulation of efficient supervised learning algorithms for SNN is difficult and remains an important problem in the research area. This article presents SPAN – a spiking neuron that is able to learn associations of arbitrary spike trains in a supervised fashion allowing the processing of spatio-temporal information encoded in the precise timing of spikes. The idea of the proposed algorithm is to transform spike trains during the learning phase into analog signals so that common mathematical operations can be performed on them. Using this conversion, it is possible to apply the well-known Widrow-Hoff rule directly to the transformed spike trains in order to adjust the synaptic weights and to achieve a desired input/output spike behavior of the neuron. In the here presented experimental analysis, the proposed learning algorithm is evaluated regarding its learning capabilities, its memory capacity, its robustness to noisy stimuli and its classification performance. Differences and similarities of SPAN regarding two related algorithms, ReSuMe and Chronotron, are discussed.
dc.publisher World Scientific Publishing Company
dc.publisher AUT University
dc.relation.isreplacedby 10292/4620
dc.relation.isreplacedby http://hdl.handle.net/10292/4620
dc.relation.uri http://dx.doi.org/10.1142/S0129065712500128
dc.rights Electronic version of an article published as [see Citation] [see Publisher version] © [copyright World Scientific Publishing Company] [see Publisher version]
dc.subject Spiking Neural Network; Temporal coding; Spike pattern association; Learning
dc.title SPAN: Spike Pattern Association Neuron for learning spatio-temporal sequences
dc.type Journal Article
dc.rights.accessrights OpenAccess
dc.identifier.doi 10.1142/S0129065712500128
aut.relation.endpage 16
aut.relation.issue 4 (2012)
aut.relation.startpage 1
aut.relation.volume 22

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