Asynchronous updating of threshold-coupled chaotic neurons

We study a network of chaotic model neurons incorporating threshold-activated coupling. We obtain a wide range of spatiotemporal patterns under varying degrees of asynchronicity in the evolution of the neuronal components. For instance, we find that sequential updating of threshold-coupled chaotic neurons can yield dynamical switching of the individual neurons between two states. So varying the asynchronicity in the updating scheme can serve as a control mechanism to extract different responses, and this can have possible applications in computation and information processing.