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Geosciences 2017, 7(3), 49;

Evolution of Neural Dynamics in an Ecological Model

School of Informatics and Computing, Indiana University, 919 E. 10th St., Bloomington, IN 47408, USA
Author to whom correspondence should be addressed.
Received: 14 March 2017 / Revised: 20 June 2017 / Accepted: 27 June 2017 / Published: 4 July 2017
(This article belongs to the Special Issue Individual-Based Ecological Modeling)
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What is the optimal level of chaos in a computational system? If a system is too chaotic, it cannot reliably store information. If it is too ordered, it cannot transmit information. A variety of computational systems exhibit dynamics at the “edge of chaos”, the transition between the ordered and chaotic regimes. In this work, we examine the evolved neural networks of Polyworld, an artificial life model consisting of a simulated ecology populated with biologically inspired agents. As these agents adapt to their environment, their initially simple neural networks become increasingly capable of exhibiting rich dynamics. Dynamical systems analysis reveals that natural selection drives these networks toward the edge of chaos until the agent population is able to sustain itself. After this point, the evolutionary trend stabilizes, with neural dynamics remaining on average significantly far from the transition to chaos. View Full-Text
Keywords: agent-based modeling; artificial life; neural networks; evolution; chaos agent-based modeling; artificial life; neural networks; evolution; chaos

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Williams, S.; Yaeger, L. Evolution of Neural Dynamics in an Ecological Model. Geosciences 2017, 7, 49.

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