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Lattice physics approaches for neural networks.

Giampiero Bardella1, Simone Franchini1, Pierpaolo Pani1

  • 1Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy.

Iscience
|December 16, 2024
PubMed
Summary

We introduce a mathematical framework using lattice field theory to model neural networks. This approach connects physical principles to neural activity, aiding in understanding complex brain functions.

Keywords:
Computing methodologyMathematical method in physicsNeuroscience

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Area of Science:

  • Neuroscience
  • Theoretical Physics
  • Complex Systems Science

Background:

  • Modern neuroscience integrates diverse disciplines, leading to new theoretical frameworks.
  • Physics and complex systems science significantly influence novel conceptualizations in neuroscience.
  • A mathematical framework for neural spatiotemporal interactions is needed.

Purpose of the Study:

  • To provide an intuitive summary of a lattice field theory-based mathematical framework for neuroscience.
  • To illustrate the connection between the framework's parameters and experimental variables.
  • To enable the description of neural networks using lattice physics principles.

Main Methods:

  • Application of lattice field theory, a paradigm from theoretical particle physics.
  • Development of a mathematical framework for spatiotemporal neural interactions.
  • Utilizing renormalization procedures to link theoretical parameters with experimental data.

Main Results:

  • A concise synopsis of the lattice physics approach to neural networks.
  • Demonstration of how to connect theoretical parameters to experimental variables.
  • Key concepts for describing neural networks via lattice physics are presented.

Conclusions:

  • The presented framework offers a novel way to model neural networks using physical principles.
  • This approach is relevant given advancements in computational power.
  • It facilitates the linkage of observed neural activity to generative models based on physics.