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.