Neural network architectures for associative memory

This paper identifies optimal strategies available for the computation of synaptic weights in both auto- and hetero-associative networks. An iterative algorithm is proposed to train fully-connected feedback networks and it is shown that the recall performance can be predicted without recourse to protracted simulation studies. More importantly, the vast superiority of Hamming-type networks for binary pattern classification, over the corresponding neural network algorithms which rely instead on the distributed storage of data is confirmed