Journal of Statistical Physics, Vol. 58, Nos. 5/6, 1990, p. 1257-1266.
The effect of damage on the pattern recognition in the Hopfield-model of neural networks is studied. It is assumed that in a damaged network the synaptic efficacies J(i, j)= J(j, i) between pairs of neurons S(i) and S(j) follow the Hebb rule with probability (1 -p) and are equal to zero with probability p. Numerical simulations are performed for a net consisting of 400 neurons. It is investigated in detail how the retrieval of noisy patterns and the storage capacity of the net depends, for varying initial noise, on the concentration p of the damaged synaptic efficacies.