On the associative memory design for the Hopfield neural network

The authors examine the selection of connection weights of a Hopfield neural network model so that the network functions as a content addressable memory (CAM). They consider the discrete time version with synchronous update rule and sigmoid type nonlinear functions in the neuron outputs. The general characterization of connection weights for fixed-point programming and a condition for the asymptotic stability of these fixed points are presented. An example is also included for the analysis. It was shown that a single choice of connection weights is dependent upon two matrices, whose choice will affect the network functioning properly as a CAM