Navigation and Cognitive Map Formation Using Aperiodic Neurodynamics

Biological brains are saturated with complex dynamics. Artificial neural network models abstract much of this complexity away and represent the computational process of neuronal groups in terms of simple point, and sometimes periodic attractors. But is this abstraction justified? Aperiodic dynamics are known to be essential in the formation of perceptual mechanisms and representations in biological organisms. Advances in neuroscience and computational neurodynamics are helping us to understand the properties of nonlinear systems that are fundamental in the self-organization of stable, complex patterns for perceptual, memory and other cognitive mechanisms in biological brains.

Much of this new understanding of the principles of self-organization in biological brains has yet to be used to improve the performance of animats and other biologically inspired models of behavior generation. In this paper we review some of the findings of how biological brains may use aperiodic dynamics in the formation of perceptuai mechanisms. We discuss some models of this formation of chaotic attractors for perceptual categorization. And finally we present some work using these models to develop cognitive maps and navigation behavior in an autonomous agent.