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  3. Energy Landscape Analysis Based On The Ising Model: Tutorial Review.
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  3. Energy Landscape Analysis Based On The Ising Model: Tutorial Review.

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Energy landscape analysis based on the Ising model: Tutorial review.

Naoki Masuda1,2, Saiful Islam2, Si Thu Aung1

  • 1Department of Mathematics, State University of New York at Buffalo.

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|May 29, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This review details an energy landscape analysis method using the Ising model for multivariate time series data. It visualizes data dynamics as trajectories on a computed energy landscape, expanding beyond fMRI applications.

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Area of Science:

  • Computational Neuroscience
  • Data Analysis
  • Statistical Physics

Background:

  • Energy landscape analysis methods are crucial for understanding complex system dynamics.
  • The Ising model provides a framework for representing interactions within systems.
  • Multivariate time series data capture dynamic processes across various scientific domains.

Purpose of the Study:

  • To provide a comprehensive tutorial on energy landscape analysis using the Ising model.
  • To explain the underlying concepts, terminology, and validation procedures.
  • To highlight emerging applications beyond functional magnetic resonance imaging (fMRI) and neuroscience.

Main Methods:

  • Utilizes the Ising model to estimate energy landscapes from multivariate time series data.
  • Models data dynamics as trajectories moving between basins on the landscape.
  • Reviews computational steps, theoretical underpinnings, and validation techniques.

    • Main Results:

    • The Ising model-based energy landscape analysis offers a robust method for capturing system dynamics.
    • The approach facilitates understanding transitions between different states or 'basins'.
    • Demonstrates applicability to diverse datasets, including but not limited to fMRI data.

    Conclusions:

    • This review serves as a guide for researchers applying energy landscape analysis to new data types.
    • The method's versatility supports its adoption in fields beyond neuroscience.
    • Understanding energy landscapes is key to interpreting complex temporal data patterns.