A novel chaotic neural network architecture

The basic premise of this research is that deterministic chaos is a powerful mechanism for the storage and retrieval of information in the dynamics of artificial neural networks. Substantial evidence has been found in biological studies for the presence of chaos in the dynamics of natural neuronal systems [1-3]. Many have suggested that this chaos plays a central role in memory storage and retrieval [1,4-6]. Indeed, chaos offers many advantages over alternative memory storage mechanisms used in artificial neural networks. One is that chaotic dynamics are significantly easier to control than other linear or non-linear systems, requiring only small appropriately timed perturbations to constrain them within specific Unstable Periodic Orbits (UPOs). Another is that chaotic attractors contain an infinite number of these UPOs. If individual UPOs can be made to represent specific internal memory states of a system, then in theory a chaotic attractor can provide an infinite memory store for the system. In this paper we investigate the possibility that a network can self-select UPOs in response to specific dynamic input signals. These UPOs correspond to network recognition states for these input signals.