SPAN: a neuron for precise-time spike pattern association

Authors:
Ammar Mohemmed;Stefan Schliebs;Nikola Kasabov
Affiliations:
Knowledge Engineering Discovery Research Institute, Auckland, New Zealand;Knowledge Engineering Discovery Research Institute, Auckland, New Zealand;Knowledge Engineering Discovery Research Institute, Auckland, New Zealand
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

In this paper we propose SPAN, a LIF spiking neuron that is capable of learning input-output spike pattern association using a novel learning algorithm. The main idea of SPAN is transforming the spike trains into analog signals where computing the error can be done easily. As demonstrated in an experimental analysis, the proposed method is both simple and efficient achieving reliable training results even in the context of noise.