PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Classification of Distorted Patterns by Feed-forward Spiking Neural Networks
Ioana Sporea and Andre Gruning
In: International Conference on Artificial Neural Networks, Sept 2012, Lausanne.


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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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:9577
Deposited By:Andre Gruning
Deposited On:22 September 2012