Evolution of Neural Dynamics in an Ecological Model

Authored by Steven Williams, Larry Yaeger

Date Published: 2017

DOI: 10.3390/geosciences7030049

Sponsors: National Academies Keck Future Initiatives United States National Science Foundation (NSF)

Platforms: C++

Model Documentation: Other Narrative Mathematical description

Model Code URLs: https://github.com/polyworld/polyworld

Abstract

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.

Tags

Agent-based modeling Complexity Evolution networks Chaos Neural networks information Artificial life Computation Brain Edge