ASSOCIATIVE MEMORY IN NEURAL NETWORKS WITH THE HEBBIAN LEARNING RULE

10 May 1989

journal article
Published by World Scientific Pub Co Pte Ltd in Modern Physics Letters B

Vol. 3 (7) , 555-560
https://doi.org/10.1142/s021798498900087x

Abstract

We consider the Hopfield model with the most simple form of the Hebbian learning rule, when only simultaneous activity of pre- and post-synaptic neurons leads to modification of synapse. An extra inhibition proportional to full network activity is needed. Both symmetric nondiluted and asymmetric diluted networks are considered. The model performs well at extremely low level of activity p−1/2, where K is the mean number of synapses per neuron.

Keywords

SOMATIC CELLS
NEURAL NETWORK
COMPUTER NETWORK
HEBBIAN LEARNING
MATHEMATICAL MODEL
ASSOCIATIVE MEMORY

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