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Thermodynamic Neural Network.

Entropy (Basel, Switzerland)ยท2020
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Related Experiment Video

Updated: Sep 6, 2025

Author Spotlight: Simulation and Analysis of the Temperature Rise of Ring Main Unit Equipment
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Thermodynamic State Machine Network.

Todd Hylton1

  • 1Department of Mechanical and Aerospace Engineering, University of California, San Diego, CA 92093, USA.

Entropy (Basel, Switzerland)
|June 24, 2022
PubMed
Summary

This study introduces a thermodynamic state machine network model that exhibits a phase transition to emergent spiking neural network-like dynamics, offering a new perspective on self-organization and computation.

Keywords:
active equilibrationinput functionalizationmachine learningscale integrationthermodynamic computingthermodynamicalism

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Area of Science:

  • Thermodynamics
  • Complex Systems
  • Computational Neuroscience

Background:

  • Self-organizing systems are crucial for understanding complex phenomena.
  • Thermodynamic principles can inform computational models.
  • Existing machine learning models face limitations in scalability and generality.

Purpose of the Study:

  • To develop a novel thermodynamic state machine network model.
  • To investigate emergent computational properties from thermodynamic principles.
  • To explore a new philosophical perspective, thermodynamicalism, for artificial intelligence.

Main Methods:

  • Constructed a network of probabilistic, stateful automata.
  • Applied Boltzmann statistics for equilibration and learned state transitions.
  • Incorporated four postulates of self-organizing, open thermodynamic systems.
  • Analyzed network dynamics under periodically changing inputs.

Main Results:

  • The model demonstrated a diffusive-to-mechanistic phase transition in network dynamics.
  • Evolved networks spontaneously developed structures resembling spiking neural networks.
  • Identified limitations in scalability, generality, and temporality.

Conclusions:

  • The thermodynamic state machine network can exhibit emergent computational structures.
  • Thermodynamic principles offer a viable foundation for novel computing paradigms.
  • Thermodynamicalism provides a philosophical framework for addressing AI limitations.