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Noise in the brain: a physical network model

H Haken1

  • 1Institute for Theoretical Physics and Synergetics, Stuttgart, Germany.

International Journal of Neural Systems
|September 1, 1996
PubMed
Summary
This summary is machine-generated.

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Noise in the brain is reduced by cooperative neuron activity, modeled using laser physics principles. This model reveals how synchronized neural networks can minimize system noise for better brain function.

Area of Science:

  • Neuroscience
  • Physics
  • Computational Neuroscience

Background:

  • The brain, as an open physical system, is inherently susceptible to noise.
  • Understanding and mitigating neural noise is crucial for comprehending brain function and dysfunction.

Purpose of the Study:

  • To develop an explicit physical model for neural noise based on principles from laser physics.
  • To investigate how cooperative interactions among neural components influence noise levels.
  • To establish analogies between the physical model and established neural network equations.

Main Methods:

  • An active physical system model, adapted from laser physics, was employed.
  • The model explicitly defines fluctuating forces contributing to neural noise.
  • Mathematical transformations were used to draw parallels with neural network equations, including sigmoid nonlinearities.

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Main Results:

  • Cooperation among system components (neurons) significantly reduces noise levels.
  • The correlation function between individual neural components was determined.
  • The model accurately captures nonlinear neuronal properties and propagation of excitation in axons and dendrites.

Conclusions:

  • Cooperative dynamics in neural systems can effectively suppress noise.
  • The physical model provides a framework for understanding neural noise and activity propagation.
  • Analogies with neural network equations highlight the model's relevance to computational neuroscience.