Hopfield network with O(N) complexity using a constrained backpropagation learning

A novel associative memory model is presented, which is derived from the Hopfield discrete neural network. Its architecture is greatly simplified because the number of interconnections grows only linearly with the dimensionality of the stored patterns. It makes use of a modified backpropagation algorithm as a learning tool. During the retrieval phase the network operates as an autoassociative BAM (directional associative memory), which searches for a minimum of an appropriate energy function. Computer simulations point out the good performances of the proposed learning method in terms of capacity and number of spurious stable states.<>