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Supervised synaptic weight adaptation for a spiking neuron

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4 Author(s)
Davis, B.A. ; Computational NeuroEngineering Lab., Florida Univ., Gainesville, FL, USA ; Erdogmus, D. ; Rao, Y.N. ; Principe, J.C.

A novel algorithm named Spike-LMS is described that adapts the synaptic weights of an artificial spiking neuron to produce a desired response. The derivation of Spike-LMS follows from the derivation of the least-mean squares (LMS) algorithm used in adaptive filter theory. Spike-LMS works directly in the domain of spike trains, and therefore makes no assumptions about any particular neural encoding method. This algorithm is able to identify the synaptic weights of a spiking neuron given the pre-synaptic and post-synaptic spike trains.

Published in:

Neural Networks, 2003. Proceedings of the International Joint Conference on  (Volume:4 )

Date of Conference:

20-24 July 2003