Discrete Dynamical Networks and Their Attractor Basins

A key notion in the study of network dynamics is that state-space is connected into basins of attraction. Convergence in attractor basins correlates with order-complexity-chaos measures on space-time patterns. A network's "memory," its ability to categorize, is provided by the configuration of its separate basins, trees and sub-trees. Based on computer simulations using the software Discrete Dynamics Lab, this paper provides an overview of recent work describing some of the issues, methods, measures, results, applications and conjectures. To appear in the proceedings of Complex Systems '98University of New South Wales, Sidney, Australia.

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