Nonlinear dynamics and chaos in information processing neural networks

We consider a number of possible roles of complex dynamics and chaos in information processing by neural networks. First, we review the working principles of some well-known neural networks, and then discuss a number of approaches to utilization of chaos in neural networks. Our main goal is to present a novel view of the problem of chaos in information processing. We demonstrate that chaos emerges naturally in controls when a neural network forms a controlling part of a more complex system. We show that such neural networks can enhance efficiency by using chaos for explorations in a method known as Reinforcement Learning. A discussion on Hamiltonian neural networks is also included.