A gradient descent rule for spiking neurons emitting multiple spikes

Authors:
Olaf Booij;Hieu tat Nguyen
Affiliations:
Intelligent Sensory Information Systems, University of Amsterdam, Faculty of Science, Kruislaan 403, 1098 SJ, Amsterdam, The Netherlands;Intelligent Sensory Information Systems, University of Amsterdam, Faculty of Science, Kruislaan 403, 1098 SJ, Amsterdam, The Netherlands
Venue:
Information Processing Letters - Special issue on applications of spiking neural networks
Year:
2005

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Abstract

A supervised learning rule for Spiking Neural Networks (SNNs) is presented that can cope with neurons that spike multiple times. The rule is developed by extending the existing SpikeProp algorithm which could only be used for one spike per neuron. The problem caused by the discontinuity in the spike process is counteracted with a simple but effective rule, which makes the learning process more efficient. Our learning rule is successfully tested on a classification task of Poisson spike trains. We also applied the algorithm on a temporal version of the XOR problem and show that it is possible to learn this classical problem using only one spiking neuron making use of a hair-trigger situation.