Training Spiking Neural Networks to Associate Spatio-temporal Input-output Spike Patterns

Mohemmed, A
Schliebs, S
Matsuda, S
Kasabov, N
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In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input–output spike trains) we have proposed a supervised learning algorithm based on temporal coding to train a spiking neuron to associate input spatiotemporal spike patterns to desired output spike patterns. The algorithm is based on the conversion of spike trains into analogue signals and the application of the Widrow–Hoff learning rule. In this paper we present a mathematical formulation of the proposed learning rule. Furthermore, we extend the application of the algorithm to train a SNN consisting of multiple spiking neurons to perform spatiotemporal pattern classification and we show that the accuracy of classification is improved significantly over a single spiking neuron. We also investigate a number of possibilities to map the temporal output of the trained spiking neuron into a class label. Potential applications for motor control in neuro-rehabilitation and neuro-prosthetics are discussed as a future work.

Spiking Neural Networks , Supervised learning , Spatio-temporal control , Temporal coding
Neurocomputing, vol.107, pp.3 - 10
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Copyright © 2013 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version)