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SECOND ORDER NEURONS AND LEARNING IN COHEN-GROSSBERG NETWORKS

K. GOPALSAMY

School of Informatics and Engineering, Flinders University, G.P.O. Box 2100, Adelaide, Australia

https://doi.org/10.1142/S0129065706000597Cited by:6 (Source: Crossref)

Abstract

The well known Cohen-Grossberg network is modified to include second order neural interconnections and also to have a learning component. Sufficient conditions are obtained for the existence of a globally exponentially stable equilibrium. The model provides a two-fold generalization of the Cohen-Grossberg network in the sense if one removes the learning component, then one gets a network with second order synaptic interactions; if both the learning component and the second order interactions are removed, then the model reduces to the standard Cohen-Grossberg network.

Keywords:

AMSC: 92B20, 34K20

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