Weight error sensitivity of fixed point attractors in associative memory networks

The sensitivity of Hopfield neural networks, with two-state neurons is investigated. Simple expressions are derived which give the probability that an equilibrium point of the nominal connection matrix remains a fixed point and an attractor, as a function of the relative error in the weights. Such probability decreases as the number of neurons in the network and the number of stored patterns increase.