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. © 2012 Springer-Verlag.
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CITATION STYLE
Sporea, I., & Grüning, A. (2012). Classification of distorted patterns by feed-forward spiking neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7552 LNCS, pp. 264–271). https://doi.org/10.1007/978-3-642-33269-2_34
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