title: Covariance learning of correlated patterns in competitive networks.
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.
authors.affiliation: Department of Electrical and Computer Engineering and Computer Science, University of Cincinnati, OH 45221, USA.
authors.forename: A A
authors.initials: AA
authors.lastname: Minai
mesh_terms: D001185:Artificial Intelligence; D004594:Electrophysiology; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D013569:Synapses / Q000502:physiology*