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Modeling of a Neural System Based on Statistical Mechanics.

Myoung Won Cho1, Moo Young Choi2

  • 1Department of Global Medical Science, Sungshin Women's University, Seoul 01133, Korea.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

The Feynman machine, a novel statistical-mechanics model, explains brain function by minimizing free energy. It links neural firing and learning, like spike-timing-dependent plasticity, to this principle using explicit time variables.

Keywords:
free-energy minimization principleneural network modelstatistical mechanics

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Area of Science:

  • Computational Neuroscience
  • Statistical Physics
  • Theoretical Biology

Background:

  • Free energy minimization is a key principle for understanding brain function and structure.
  • Statistical-mechanics-based neural network models offer insights into neural firing and learning.
  • Defining free energy in neural systems is complex, with previous models facing challenges.

Purpose of the Study:

  • To review previous statistical-mechanics models of neural systems.
  • To examine how the Feynman machine overcomes limitations of prior models.
  • To demonstrate the link between the Feynman machine's free energy minimization and biological neural processes.

Main Methods:

  • Review of existing statistical-mechanics neural network models.
  • Analysis of the Feynman machine's free energy definition using Feynman path integrals.
  • Derivation of neural firing and learning rules from free energy extremum states.

Main Results:

  • The Feynman machine successfully defines neural system free energy using explicit time variables.
  • The model demonstrates that spike-timing-dependent plasticity relates to free energy minimization.
  • Computational and learning mechanisms are based on precise spike timings.

Conclusions:

  • The Feynman machine provides a robust framework for understanding brain function through free energy minimization.
  • Explicit time variables are crucial for accurately modeling neural systems.
  • This approach offers new perspectives on biological learning mechanisms and brain phenomena.