ArticleNature communications2021
A unifying framework for mean-field theories of asymmetric kinetic Ising systems.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A note on the dynamics of extended-context disordered kinetic spin models.Journal of physics. A, Mathematical and theoretical · 2026Article
- Modeling and inferring metacommunity dynamics with Maximum Caliber.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilibrium neuronal dynamics.Nature communications · 2025Article
- Hidden high-risk states identification from routine urban traffic.PNAS nexus · 2025Article
- Inferring structure of cortical neuronal networks from activity data: A statistical physics approach.PNAS nexus · 2025Article
- Lattice physics approaches for neural networks.iScience · 2024Review
- Heavy-tailed neuronal connectivity arises from Hebbian self-organization.Nature physics · 2024Article
- Nonequilibrium Thermodynamics of the Majority Vote Model.Entropy (Basel, Switzerland) · 2023Article
- Nonequilibrium thermodynamics of the asymmetric Sherrington-Kirkpatrick model.Nature communications · 2023Article
- Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model.Scientific reports · 2022Article
- Optimal Population Coding for Dynamic Input by Nonequilibrium Networks.Entropy (Basel, Switzerland) · 2022Article
- A unifying framework for mean-field theories of asymmetric kinetic Ising systems.Nature communications · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Kinetic Ising models are powerful tools for studying the non-equilibrium dynamics of complex systems. As their behavior is not tractable for large networks, many mean-field methods have been proposed for their analysis, each based on unique assumptions about the system's temporal evolution. This disparity of approaches makes it challenging to systematically advance mean-field methods beyond previous contributions. Here, we propose a unifying framework for mean-field theories of asymmetric kinetic Ising systems from an information geometry perspective. The framework is built on Plefka expansions of a system around a simplified model obtained by an orthogonal projection to a sub-manifold of tractable probability distributions. This view not only unifies previous methods but also allows us to develop novel methods that, in contrast with traditional approaches, preserve the system's correlations. We show that these new methods can outperform previous ones in predicting and assessing network properties near maximally fluctuating regimes.
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Registered trials
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