
Simplicial Kuramoto models have emerged as a diverse and intriguing class of models describing oscillators on simplices rather than nodes. In this paper, we present a unified framework to describe different variants of these models, categorized into three main groups: “simple” models, “Hodge-coupled” models, and “order-coupled” (Dirac) models. Our framework is based on topology and discrete differential geometry, as well as gradient systems and frustrations, and permits a systematic analysis of their properties. We establish an equivalence between the simple simplicial Kuramoto model and the standard Kuramoto model on pairwise networks under the condition of manifoldness of the simplicial complex. Then, starting from simple models, we describe the notion of simplicial synchronization and derive bounds on the coupling strength necessary or sufficient for achieving it. For some variants, we generalize these results and provide new ones, such as the controllability of equilibrium solutions. Finally, we explore a potential application in the reconstruction of brain functional connectivity from structural connectomes and find that simple edge-based Kuramoto models perform competitively or even outperform complex extensions of node-based models.
Physics - Physics and Society, Kuramoto models, Nonlinear oscillations and coupled oscillators for ordinary differential equations, Synchronization of solutions to ordinary differential equations, FOS: Physical sciences, Physics and Society (physics.soc-ph), Neural networks for/in biological studies, artificial life and related topics, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Phase transitions, Computational neuroscience, Network theory, FOS: Mathematics, Algebraic Topology (math.AT), Mathematics - Algebraic Topology, Algebraic topology, Adaptation and Self-Organizing Systems (nlin.AO), Optimization problems
Physics - Physics and Society, Kuramoto models, Nonlinear oscillations and coupled oscillators for ordinary differential equations, Synchronization of solutions to ordinary differential equations, FOS: Physical sciences, Physics and Society (physics.soc-ph), Neural networks for/in biological studies, artificial life and related topics, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Phase transitions, Computational neuroscience, Network theory, FOS: Mathematics, Algebraic Topology (math.AT), Mathematics - Algebraic Topology, Algebraic topology, Adaptation and Self-Organizing Systems (nlin.AO), Optimization problems
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 21 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
