Boolean Hebb rule for binary associative memory design

Proposed a binary associative memory design method to be applied to a class of dynamic neural networks. The method is based on introducing the memory vectors as maximal independent sets to an undirected graph and on designing a dynamic network in order to find a maximal independent set whose characteristic vector is close to the given distorted vector. We show that the method provides the attractiveness for each memory vector and avoids the occurrence of spurious states whenever the set of given memory vectors satisfies certain compatibility conditions. We also analyze the application of this design method to the discrete Hopfield network.