Binary system modes of matrix-coupled multidimensional oscillators

Chongzhi Wang, Haibin Shao, Ying Tan, Dewei Li
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Abstract

The standard Kuramoto model has been instrumental in explaining synchronization and desynchronization, two emergent phenomena often observed in biological, neuronal, and physical systems. While the Kuramoto model has turned out effective with one-dimensional oscillators, real-world systems often involve high-dimensional interacting units, such as biological swarms, necessitating a model of multidimensional oscillators. However, existing high-dimensional generalizations of the Kuramoto model commonly rely on a scalar-valued coupling strength, which limits their ability to capture the full complexity of high-dimensional interactions. This work introduces a matrix, A, to couple the interconnected components of the oscillators in a d-dimensional space, leading to a matrix-coupled multidimensional Kuramoto model that approximates a prototypical swarm dynamics by its first-order harmonics. Moreover, the matrix A introduces an inter-dimensional higher-order interaction that partly accounts for the emergence of 2^{d} system modes in a d-dimensional population, where each dimension can either be synchronized or desynchronized, represented by a set of almost binary order parameters. The binary system modes capture characteristic swarm behaviors such as fish milling or polarized schooling. Additionally, our findings provides a theoretical analogy to cerebral activity, where the resting state and the activated state coexist unihemispherically. It also suggests a new possibility for information storage in oscillatory neural networks.
矩阵耦合多维振荡器的二元系统模式
标准的仓本模型有助于解释同步和非同步现象,这是在生物、神经元和物理系统中经常观察到的两种突发现象。虽然仓本模型对一维振荡器非常有效,但现实世界的系统往往涉及高维相互作用单元,如生物群,因此需要一个多维振荡器模型。然而,现有的仓本模型高维泛化通常依赖于标量值耦合强度,这限制了它们捕捉高维相互作用全部复杂性的能力。这项研究引入了一个矩阵 A,以耦合 d 维空间中振荡器的相互连接部分,从而建立了一个矩阵耦合多维仓本模型,该模型通过一阶谐波逼近原型蜂群动力学。此外,矩阵 A 引入了维间高阶相互作用,部分解释了 d 维群中 2^{d} 系统模式的出现,其中每个维度可以同步或非同步,由一组几乎二进制的阶次参数表示。二元系统模式捕捉到了鱼群的特征行为,如鱼类的碾磨或极化的游弋。此外,我们的发现还从理论上对大脑活动进行了类比,在大脑活动中,静止状态和激活状态单半球共存。这也为振荡神经网络的信息存储提供了一种新的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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