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Energy landscape analysis based on the Ising model: Tutorial review

Author

Listed:
  • Naoki Masuda
  • Saiful Islam
  • Si Thu Aung
  • Takamitsu Watanabe

Abstract

We review a class of energy landscape analysis method that uses the Ising model and takes multivariate time series data as input. The method allows one to capture dynamics of the data as trajectories of a ball from one basin to a different basin to yet another, constrained on the energy landscape specified by the estimated Ising model. While this energy landscape analysis has mostly been applied to functional magnetic resonance imaging (fMRI) data from the brain for historical reasons, there are emerging applications outside fMRI data and neuroscience. To inform such applications in various research fields, this review paper provides a detailed tutorial on each step of the analysis, terminologies, concepts underlying the method, and validation, as well as recent developments of extended and related methods.

Suggested Citation

  • Naoki Masuda & Saiful Islam & Si Thu Aung & Takamitsu Watanabe, 2025. "Energy landscape analysis based on the Ising model: Tutorial review," PLOS Complex Systems, Public Library of Science, vol. 2(5), pages 1-35, May.
    • Handle: RePEc:plo:pcsy00:0000039
    DOI: 10.1371/journal.pcsy.0000039
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    References listed on IDEAS

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    1. Elad Schneidman & Michael J. Berry & Ronen Segev & William Bialek, 2006. "Weak pairwise correlations imply strongly correlated network states in a neural population," Nature, Nature, vol. 440(7087), pages 1007-1012, April.
    2. Walter, J.-C. & Barkema, G.T., 2015. "An introduction to Monte Carlo methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 418(C), pages 78-87.
    3. David J. Wales & Mark A. Miller & Tiffany R. Walsh, 1998. "Archetypal energy landscapes," Nature, Nature, vol. 394(6695), pages 758-760, August.
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    Cited by:

    1. Ken Ishihara & Hideaki Shimazaki, 2025. "State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilibrium neuronal dynamics," Nature Communications, Nature, vol. 16(1), pages 1-21, December.

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