Biomorphic Dynamical Networks for Cognition and Control

Abstract

Advances in understanding the neuronal code employed by cortical networks indicate that networks of parametrically coupled nonlinear iterative maps, each acting as a bifurcation processing element, furnish a potentially powerful tool for the modeling, simulation, and study of cortical networks and the host of higher-level processing and control functions they perform. Such functions are central to understanding and elucidating general principles on which the design of biomorphic learning and intelligent systems can be based. The networks concerned are dynamical in nature, in the sense that they “compute” not only with static (fixed-point) attractors but also with dynamic (periodic and chaotic) attractors. As such, they compute with diverse attractors, and utilize transitions (bifurcation) between attractors and transient chaos to carry out the functions they perform. An example of a dynamical network, a parametrically coupled net of logistic processing elements, is described and discussed together some of its behavioural attributes that are relevant to elucidating the possible role for coherence, bifurcation, and chaos in higher-level brain functions carried out by cortical networks.