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Synergetics of brain function.

Hermann Haken1

  • 1Institute for Theoretical Physics I, Center of Synergetics, University of Stuttgart, Germany. haken@theo1.physik.uni-stuttgart.de

International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology
|March 11, 2006
PubMed
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This study applies synergetics to understand brain functions like movement and vision, viewing the brain as a self-organizing system. It models perception and neuronal activity using order parameters and network dynamics.

Area of Science:

  • Neuroscience
  • Synergetics
  • Cognitive Science

Background:

  • The brain is conceptualized as a self-organizing system operating near critical instabilities.
  • Brain functions, including movement coordination and visual perception, can be analyzed using principles of synergetics, focusing on spontaneous pattern formation.
  • Order parameters, or collective variables, govern neuronal activities, enslaving individual neurons and driving qualitative changes in behavior.

Purpose of the Study:

  • To analyze brain functions, specifically movement coordination and visual perception, through the lens of synergetics.
  • To model phenomenological aspects of visual perception, such as bistability, hysteresis, and oscillations, using order parameter properties.
  • To investigate neuronal network dynamics, including pulse-coupled neurons and pattern recognition, at a microscopic level.

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Main Methods:

  • Experimental and theoretical studies of finger movement coordination, including direct observation and magnetoencephalography (MEG) measurements.
  • Phenomenological modeling of visual perception using general properties of order parameters.
  • Microscopic analysis of pulse-coupled neural networks, considering dendritic currents and axonal pulses (spikes).
  • Mathematical formulation of quasi-attractors to describe visual system dynamics.

Main Results:

  • Synergetic principles successfully model qualitative changes in behavioral and neuronal activities.
  • Bistability, hysteresis, and oscillations in visual perception are accurately modeled at the phenomenological level.
  • Pulse-synchronization and pattern recognition in neural networks are analyzed, recovering attractor networks in the high-frequency limit.
  • The concept of quasi-attractors explains how the visual system navigates between states based on incoming signals.

Conclusions:

  • Synergetics provides a robust framework for understanding complex brain functions and self-organization.
  • Order parameters are crucial for describing both macroscopic perceptual phenomena and microscopic neuronal dynamics.
  • The study offers insights into consciousness, linearity vs. nonlinearity, the binding problem, and the psychological present through the synergetic approach.