We consider a class of systems of differential equations in R which exhibits convergent dynamics. We find a Lyapunov function and show that every bounded trajectory converges to the set of equilibria. Our result generalizes the results of Cohen and Grossberg [1] for convergent neural networks. It replaces the symmetry assumption on the matrix of weights by the assumption on the structure of the...

Chartier and his colleagues have recently proposed a nonlinear synchronous attractor neural network. In the Nonlinear Dynamic Recurrent Associative Memory (NDRAM), learning has been shown to converge to a set of real-valued attractors in single-layered neural networks and bidirectional associative memories. However, the transmission is highly nonlinear and its global stability has never been an...