A temporal learning rule in recurrent systems supports high spatio-temporal stochastic interactions

Authors:
Thomas Wennekers;Nihat Ay
Affiliations:
Centre for Theoretical and Computational Neuroscience, University of Plymouth, PL4 8AA Plymouth, United Kingdom;Mathematics Institute, Friedrich Alexander University Erlangen-Nuremberg, D-91054 Erlangen, Germany
Venue:
Neurocomputing
Year:
2006

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Abstract

The maximization of spatio-temporal stochastic interactions (called TIM) has been proposed as an information-theoretic organizing principle in neural systems which supports a high cooperativity among cells and complex correlation patterns. The present work shows that temporal learning rules induce a high (though not always maximal) stochastic interaction in Markov chains and probabilistic neural networks.