Emergence of computational chaos in asynchronous neurocomputing

One of the most important features of artificial neural networks in emerging, brain-inspired, nanoarchitectural design is their inherent ability to perform massively parallel, nonlinear signal processing. When operating in a system-wide asynchronous regime, such networks may exhibit a phenomenon referred to as "computational chaos", which impedes the efficient retrieval of information usually stored in the system\´s attractors. We illustrate the emergence of computational chaos from fixed point and limit cycle attractors for node communication delays in a widely used neural network model. In particular, the complete Lyapunov spectrum associated with the network dynamics is computed, and conditions that prevent the emergence of chaos are derived.