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Neural Computation, Vol 9, 667-681, Copyright © 1997 by The MIT Press

LETTERS

Covariance Learning of Correlated Patterns in Competitive Networks

Ali A. Minai

Covariance learning is a powerful type of Hebbian learning, allowing both potentiation and depression of synaptic strength. It is used for associative memory in feedforward and recurrent neural network paradigms. This article describes a variant of covariance learning that works particularly well for correlated stimuli in feedforward networks with competitive K-of-N firing. The rule, which is nonlinear, has an intuitive mathematical interpretation, and simulations presented in this article demonstrate its utility.


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Copyright © 1997 by The MIT Press.