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000101621 0247_ $$2doi$$a10.1038/s41467-021-20890-5
000101621 0248_ $$2sideral$$a123934
000101621 037__ $$aART-2021-123934
000101621 041__ $$aeng
000101621 100__ $$0(orcid)0000-0002-3366-4706$$aAguilera, M.
000101621 245__ $$aA unifying framework for mean-field theories of asymmetric kinetic Ising systems
000101621 260__ $$c2021
000101621 5060_ $$aAccess copy available to the general public$$fUnrestricted
000101621 5203_ $$aKinetic Ising models are powerful tools for studying the non-equilibrium dynamics of complex systems. As their behavior is not tractable for large networks, many mean-field methods have been proposed for their analysis, each based on unique assumptions about the system’s temporal evolution. This disparity of approaches makes it challenging to systematically advance mean-field methods beyond previous contributions. Here, we propose a unifying framework for mean-field theories of asymmetric kinetic Ising systems from an information geometry perspective. The framework is built on Plefka expansions of a system around a simplified model obtained by an orthogonal projection to a sub-manifold of tractable probability distributions. This view not only unifies previous methods but also allows us to develop novel methods that, in contrast with traditional approaches, preserve the system’s correlations. We show that these new methods can outperform previous ones in predicting and assessing network properties near maximally fluctuating regimes.
000101621 536__ $$9info:eu-repo/grantAgreement/EC/H2020/892715/EU/Data-driven Inference of Models from Embodied Neural Systems In Vertebrate Experiments/DIMENSIVE$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 892715-DIMENSIVE
000101621 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000101621 590__ $$a17.694$$b2021
000101621 592__ $$a4.846$$b2021
000101621 594__ $$a23.2$$b2021
000101621 591__ $$aMULTIDISCIPLINARY SCIENCES$$b6 / 74 = 0.081$$c2021$$dQ1$$eT1
000101621 593__ $$aChemistry (miscellaneous)$$c2021$$dQ1
000101621 593__ $$aBiochemistry, Genetics and Molecular Biology (miscellaneous)$$c2021$$dQ1
000101621 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000101621 700__ $$aMoosavi, S.A.
000101621 700__ $$aShimazaki, H.
000101621 773__ $$g12, 1 (2021), 1197 [12 pp]$$tNature communications$$x2041-1723
000101621 8564_ $$s1092237$$uhttps://zaguan.unizar.es/record/101621/files/texto_completo.pdf$$yVersión publicada
000101621 8564_ $$s1387514$$uhttps://zaguan.unizar.es/record/101621/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000101621 909CO $$ooai:zaguan.unizar.es:101621$$particulos$$pdriver
000101621 951__ $$a2023-05-18-14:08:08
000101621 980__ $$aARTICLE