Aperiodic dynamics for appetitive/aversive behavior in autonomous agents

Abstract

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 modeled or used to improve the performance of autonomous robotic and virtual agents. In this paper we present a model of an autonomous agent learning appetitive/aversive behaviors using a neuronal group model capable of such aperiodic dynamics. We demonstrate how such dynamics are useful in the self-organization of perception and behavior, and discuss the use of aperiodic dynamics in the self-organization of cognitive mechanisms in autonomous agents.