Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

March 1, 1997, Vol. 9, No. 3, Pages 667-681
(doi: 10.1162/neco.1997.9.3.667)
© 1997 Massachusetts Institute of Technology
Covariance Learning of Correlated Patterns in Competitive Networks
Article PDF (157.31 KB)
Abstract

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.