A neural network to design neural networks

The design of the Hopfield associative memory is reformulated in terms of a constraint satisfaction problem. An electronic neural net capable of solving this problem in real time is proposed. Circuit solutions correspond to symmetrical zero-diagonal matrices that possess few spurious stable states. The stability of the net is proved using a suitable Lyapunov function, and simulation results are presented. The proposed network also permits design of an associative memory with a given set of state transitions, avoiding the computation of pseudo-inverses. The net exhibits several features that make it attractive for VLSI implementation