A unifying framework for mean-field theories of asymmetric kinetic Ising systems

Aguilera, M. ; Moosavi, S.A. ; Shimazaki, H.

Resumen: 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.

Idioma: Inglés

DOI: 10.1038/s41467-021-20890-5

Año: 2021

Publicado en: Nature communications 12, 1 (2021), 1197 [12 pp]
ISSN: 2041-1723

Factor impacto JCR: 17.694 (2021)

Categ. JCR: MULTIDISCIPLINARY SCIENCES rank: 6 / 74 = 0.081 (2021) - Q1 - T1

Factor impacto CITESCORE: 23.2 - Biochemistry, Genetics and Molecular Biology (Q1) - Physics and Astronomy (Q1)

Factor impacto SCIMAGO: 4.846 - Chemistry (miscellaneous) (Q1) - Biochemistry, Genetics and Molecular Biology (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/EC/H2020/892715/EU/Data-driven Inference of Models from Embodied Neural Systems In Vertebrate Experiments/DIMENSIVE

Tipo y forma: Artículo (Versión definitiva)