Information storage and retrieval in spin-glass like neural networks

Abstract

The link between the structure of a neural network and its attractor states is investigated, with a view to designing associative memories based on such networks. It is shown that, for any preassigned set of states to be memorized, the parameters of the network can be completely calculated in most cases so as to guaranteee the stability of these states. The spin glass formulation of the neural network problem leads to particularly simple results which, in some cases, allow an analytical evaluation of the attractivity of the memorized states.