ASSOCIATIVE MEMORY IN NEURAL NETWORKS WITH THE HEBBIAN LEARNING RULE
Misha V. Tsodyks · Modern Physics Letters B · 1989
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.