Stabilization role of inhibitory self-connections in a delayed neural network

In a delayed Hopfield neural network that is strongly connected with non-inhibitory interconnections, fast and inhibitory self-connections lead to global convergence to a unique equilibrium of the network. By applying monotone dynamical systems theory and an embedding technique, we prove that this conclusion remains true without the requirement of strong connectivity or non-inhibitory interconnections.