Training spiking neural networks to associate spatio-temporal input-output spike patterns

Authors:
Ammar Mohemmed;Stefan Schliebs;Satoshi Matsuda;Nikola Kasabov
Affiliations:
Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand;Department of Mathematical Information Engineering, Nihon University, Japan;Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Private Bag 92006, Auckland 1142, New Zealand and Institute for Neuroinformatics, ETH and University of Z ...
Venue:
Neurocomputing
Year:
2013

Citing 3
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Abstract

In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input-output spike trains) [1] 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.