Tempotron-Like Learning with ReSuMe

Authors:
Răzvan V. Florian
Affiliations:
Center for Cognitive and Neural Studies (Coneural), Cluj-Napoca, Romania 400487 and Department of Computer Science, Babeş-Bolyai University, Cluj-Napoca, Romania 400084
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Year:
2008

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Abstract

The tempotron is a model of supervised learning that allows a spiking neuron to discriminate between different categories of spike trains, by firing or not as function of the category. We show that tempotron learning is quasi-equivalent to an application for a specific problem of a previously proposed, more general and biologically plausible, supervised learning rule (ReSuMe). Moreover, we show through simulations that by using ReSuMe one can train neurons to categorize spike trains not only by firing or not, but also by firing given spike trains, in contrast to the original tempotron proposal.