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Classification of Distorted Patterns by Feed-forward Spiking Neural Networks

Sporea, I and Grüning, A (2012) Classification of Distorted Patterns by Feed-forward Spiking Neural Networks In: International Conference on Articifial Neural Networks 2012, 2012-09-11 - 2012-09-14, Lausanne, Switzerland.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Sporea, IUNSPECIFIEDUNSPECIFIED
Grüning, AUNSPECIFIEDUNSPECIFIED
Date : 2012
Identification Number : 10.1007/978-3-642-33269-2_34
Contributors :
ContributionNameEmailORCID
PublisherSpringer, UNSPECIFIEDUNSPECIFIED
Additional Information : The original publication is available at http://www.springerlink.com
Depositing User : Symplectic Elements
Date Deposited : 25 Feb 2014 14:31
Last Modified : 09 Jun 2014 13:29
URI: http://epubs.surrey.ac.uk/id/eprint/713371

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