Classification of distorted patterns by feed-forward spiking neural networks

Authors:
Ioana Sporea;André Grüning
Affiliations:
Department of Computing, University of Surrey, Guildford, United Kingdom;Department of Computing, University of Surrey, Guildford, United Kingdom
Venue:
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Year:
2012

Citing 6
Cited 0

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Abstract

In this paper, a feed forward spiking neural network is tested with spike train patterns with additional and missing spikes. The network is trained with noisy and distorted patterns with an extension of the ReSuMe learning rule to networks with hidden layers. The results show that the multilayer ReSuMe can reliably learn to discriminate highly distorted patterns spanning over 500 ms.