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Learning with "Relevance": Using a Third Factor to Stabilize Hebbian Learning

B.Porr{at}elec.gla.ac.uk Department of Electronics and Electrical Engineering, University of Glasgow, Glasgow, GT12 8LT, Scotland

Florentin Wörgötter

worgott{at}bccn-goettingen.de Bernstein Centre for Computational Neuroscience, University of Göttingen, 37073 Göttingen, Germany

It is a well-known fact that Hebbian learning is inherently unstable because of its self-amplifying terms: the more a synapse grows, the stronger the postsynaptic activity, and therefore the faster the synaptic growth. This unwanted weight growth is driven by the autocorrelation term of Hebbian learning where the same synapse drives its own growth. On the other hand, the cross-correlation term performs actual learning where different inputs are correlated with each other. Consequently, we would like to minimize the autocorrelation and maximize the cross-correlation. Here we show that we can achieve this with a third factor that switches on learning when the autocorrelation is minimal or zero and the cross-correlation is maximal. The biological counterpart of such a third factor is a neuromodulator that switches on learning at a certain moment in time. We show in a behavioral experiment that our three-factor learning clearly outperforms classical Hebbian learning.