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Brain chaos and computation

A Babloyantz1, C Lourenço

  • 1Center for Nonlinear Phenomena and Complex Systems, Université Libre de Bruxelles, Belgium.

International Journal of Neural Systems
|September 1, 1996
PubMed
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This study introduces a model cortex with two chaotic networks capable of pattern discrimination and motion detection. Cognitive functions emerge when one network enters an "attentive" state via stabilized periodic orbits.

Area of Science:

  • Computational neuroscience
  • Complex systems theory

Background:

  • The brain's ability to process complex information relies on intricate network dynamics.
  • Understanding the neural basis of cognitive functions like attention and motion detection is a key challenge.

Purpose of the Study:

  • To investigate a computational model of the cortex exhibiting cognitive capabilities.
  • To explore the role of chaotic dynamics and network states in enabling pattern recognition and motion perception.

Main Methods:

  • Development of a computational model featuring two interconnected spatiotemporal chaotic networks.
  • Analysis of network dynamics to identify conditions for cognitive function emergence.

Main Results:

  • The model successfully discriminates between input patterns.

Related Experiment Videos

  • The system demonstrates the ability to detect motion and estimate its velocity.
  • Cognitive functions are contingent upon the emergence of an "attentive" state through periodic orbit stabilization within a chaotic network.

    • Conclusions:

    • Chaotic neural networks can support sophisticated cognitive processes.
    • Network state transitions, specifically to stabilized periodic orbits, are crucial for attention and sensory processing.
    • This model provides insights into the neural mechanisms underlying perception and cognition.