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Exploring Criticality as a Generic Adaptive Mechanism.

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Summary
This summary is machine-generated.

Biological and cognitive systems near criticality exhibit autonomous behavior. This study develops a learning model where embodied agents adapt towards critical points, enhancing their behavioral flexibility.

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Area of Science:

  • Computational Neuroscience
  • Complex Systems Theory
  • Artificial Intelligence

Background:

  • Biological and cognitive systems often operate near critical points, the boundaries between different activity phases.
  • This proximity to criticality is hypothesized to be linked to autonomous behavior generation, though evidence is speculative.

Purpose of the Study:

  • To explore the connection between criticality and autonomous behavior in embodied agents.
  • To develop a learning model that drives agents towards critical points using maximum entropy principles.

Main Methods:

  • Utilized maximum entropy models to identify indicators of criticality.
  • Derived a learning model within an embodied Boltzmann machine, employing gradient ascent to maximize heat capacity.
  • Implemented the model in a Mountain Car benchmark test with a neural controller.

Main Results:

  • The neural controller achieved an apparent critical point, coinciding with a behavioral regime transition.
  • Synergistic information between sensors and network components was maximized at this critical point.
  • The embodied agent demonstrated flexible behavioral patterns.

Conclusions:

  • The developed learning model successfully drives embodied agents toward criticality.
  • This approach provides a framework for understanding the link between criticality and autonomous behavior in living systems.
  • Critical agents show potential for acquiring adaptive strategies in diverse contexts.