A dynamic approach to invariant extraction from time-varying inputs by using chaos in neural nets

The authors briefly summarize the main lines of the convergent and chaotic-bifurcative approaches in neural networks, and present a general model founded on an informational use of a chaotic dynamics. It exploits the inner fine structure of unstable periodic orbits of a chaotic dynamics to perform invariant extractions and reconstruction tasks in a dynamic way from a complex time-varying (at least chaotic) input. The neurophysiological background (i.e. synchronization behavior and functional segregation in the sensory cortex) is discussed. The proposed approach suggests that there exists a strict relationship in chaotic systems between dynamic reconstruction, optimization, and stabilization intended as a relaxation process in as much as they are all functions of an inner self-correlation process. This may depend on the fact that chaos, owing to its ultimate deterministic nature, is an intelligent noise. In the fine structure of its invariants, it retains a memory of its evolution