Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Learning in a competitive network
Neural Networks
Feature discovery by competitive learning
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neurocomputing: foundations of research
Tests on cell assembly theory of the action of the brain, using a large digital computer
Neurocomputing: foundations of research
A Neural Network for PCA and Beyond
Neural Processing Letters
Journal of Systems Architecture: the EUROMICRO Journal
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In this paper a new associative-learning algorithm, Competitive Hebbian Learning, is developed and then applied to several demonstration problems. Competitive Hebbian Learning is a modified Hebbian-learning rule; the Hebbian-type changes in weights into a node are reduced in magnitude as the simultaneous activity of the other nodes in the system increases. The algorithm shares both the Hebbian-learning property of maximizing squared node response, and the property of competitive algorithms that nodes learn to respond to different aspects of the training set. The demonstrations show that Competitive Hebbian Learning is effective in finding structure in the correlations of input vector components, in separating differing, but nonorthogonal input vectors, in finding useful single-layer functions which could be applied to the solution of Boolean-algebra problems, and in finding solutions to an approximate image-compression task.