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Towards unconventional computing through simulated evolution: control of nonlinear media by a learning classifier

Larry Bull1, Adam Budd, Christopher Stone

  • 1Faculty of Computing, Engineering & Mathematics, University of the West of England, Coldharbour Lane, Frenchay, Bristol BS16 1QY, UK. larry.bull@uwe.ac.uk

Artificial Life
|March 12, 2008
PubMed
Summary

Evolutionary learning controls nonlinear media behavior for unconventional computing. This study demonstrates directing chemical reaction waves and neuronal network stimulation using learning classifier systems.

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Area of Science:

  • Nonlinear dynamics
  • Computational intelligence
  • Biophysics

Background:

  • Nonlinear media exhibit complex spatiotemporal behaviors.
  • Controlling these behaviors is crucial for advanced computing paradigms.
  • Evolutionary learning offers a potential mechanism for adaptive control.

Purpose of the Study:

  • To investigate the application of evolutionary learning for controlling nonlinear media.
  • To explore the realization of unconventional computing through adaptive control.
  • To demonstrate control over chemical reactions and neuronal networks.

Main Methods:

  • Utilized a light-sensitive Belousov-Zhabotinsky reaction with a learning classifier system.
  • Employed dynamic light intensity control in a checkerboard grid to guide wave fragments.
  • Applied learning classifier systems to control electrical stimulation of cultured neuronal networks.

Main Results:

  • Successfully directed wave fragments to arbitrary positions in simulated and real chemical systems.
  • Demonstrated elementary learning in cultured neuronal networks via controlled electrical stimulation.
  • Identified fundamental properties of in vitro neuronal networks through learned stimulation protocols.

Conclusions:

  • Evolutionary learning provides an effective method for automatic control of nonlinear media.
  • This approach enables the development of unconventional computing systems.
  • Understanding network-specific characteristics is vital for training learning schemes.