Evolution of Neural Dynamics in an Ecological Model
AbstractWhat 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
Externally hosted supplementary file 1
Description: The source code for Polyworld
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Williams, S.; Yaeger, L. Evolution of Neural Dynamics in an Ecological Model. Geosciences 2017, 7, 49.
Williams S, Yaeger L. Evolution of Neural Dynamics in an Ecological Model. Geosciences. 2017; 7(3):49.Chicago/Turabian Style
Williams, Steven; Yaeger, Larry. 2017. "Evolution of Neural Dynamics in an Ecological Model." Geosciences 7, no. 3: 49.
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