Classification of Distorted Patterns by Feed-forward Spiking Neural Networks

I Sporea; A Grüning

doi:10.1007/978-3-642-33269-2_34

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Classification of Distorted Patterns by Feed-forward Spiking Neural Networks

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Classification of Distorted Patterns by Feed-forward Spiking Neural Networks

I Sporea and A Grüning

Proceedings of the International Conference on Articifial Neural Networks Part 1, Vol.7552(LNCS), pp.264-271

International Conference on Articifial Neural Networks 2012 (Lausanne, Switzerland, 11/09/2012 - 14/09/2012)

2012

DOI: https://doi.org/10.1007/978-3-642-33269-2_34

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.

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Title: Classification of Distorted Patterns by Feed-forward Spiking Neural Networks
Creators: I Sporea
A Grüning
Contributors: Springer (null)
Publication Details: Proceedings of the International Conference on Articifial Neural Networks Part 1, Vol.7552(LNCS), pp.264-271
Conference: International Conference on Articifial Neural Networks 2012 (Lausanne, Switzerland, 11/09/2012 - 14/09/2012)
Date published: 2012
Date submitted: 16/07/2012
Identifiers: 99511348302346
Academic Unit: Department of Computer Science
Resource Type: Conference presentation

Classification of Distorted Patterns by Feed-forward Spiking Neural Networks

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