Global exponential convergence of multitime-scale neural networks

In this paper, we investigate the convergence and stability of a neural network model with different time scales, which models the activity of cortical cognitive maps. We provide a theoretic condition for global exponential convergence of the solutions of the network, which is proved weaker than some existing results in the literature. We also introduce time-varying delays with less constraints into the neural network model and derive a general stability condition for the delay network.