Stabilizing Hopfield neural networks via inhibitory self-connections ✩

The theory of monotone dynamical systems is employed to establish some sufficient conditions for the global attractivity of the Hopfield neural networks with finite distributed delays. The results show that self-inhibitory connections can be used to stabilize a delayed network provided the diagonal delays corresponding to the inhibitory self-connections are small enough.  2003 Elsevier Inc. All rights reserved