Majhi Soumen, Ghosh Samali, Pal Palash Kumar, Pal Suvam, Pal Tapas Kumar, Ghosh Dibakar, Završnik Jernej, Perc Matjaž
Physics Department, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, 00133 Rome, Italy.
Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata 700108, India.
Phys Life Rev. 2025 Mar;52:144-170. doi: 10.1016/j.plrev.2024.12.013. Epub 2024 Dec 30.
Synchrony in neuronal networks is crucial for cognitive functions, motor coordination, and various neurological disorders. While traditional research has focused on pairwise interactions between neurons, recent studies highlight the importance of higher-order interactions involving multiple neurons. Both types of interactions lead to complex synchronous spatiotemporal patterns, including the fascinating phenomenon of chimera states, where synchronized and desynchronized neuronal activity coexist. These patterns are thought to resemble pathological states such as schizophrenia and Parkinson's disease, and their emergence is influenced by neuronal dynamics as well as by synaptic connections and network structure. This review integrates the current understanding of how pairwise and higher-order interactions contribute to different synchrony patterns in neuronal networks, providing a comprehensive overview of their role in shaping network dynamics. We explore a broad range of connectivity mechanisms that drive diverse neuronal synchrony patterns, from pairwise long-range temporal interactions and time-delayed coupling to adaptive communication and higher-order, time-varying connections. We cover key neuronal models, including the Hindmarsh-Rose model, the stochastic Hodgkin-Huxley model, the Sherman model, and the photosensitive FitzHugh-Nagumo model. By investigating the emergence and stability of various synchronous states, this review highlights their significance in neurological systems and indicates directions for future research in this rapidly evolving field.
神经元网络中的同步性对于认知功能、运动协调以及各种神经系统疾病至关重要。虽然传统研究主要关注神经元之间的成对相互作用，但最近的研究强调了涉及多个神经元的高阶相互作用的重要性。这两种相互作用都会导致复杂的同步时空模式，包括迷人的嵌合态现象，即同步和不同步的神经元活动共存。这些模式被认为类似于精神分裂症和帕金森病等病理状态，它们的出现受到神经元动力学以及突触连接和网络结构的影响。本综述整合了当前对成对和高阶相互作用如何促成神经元网络中不同同步模式的理解，全面概述了它们在塑造网络动力学中的作用。我们探索了广泛的连接机制，这些机制驱动着多样的神经元同步模式，从成对的长程时间相互作用和时延耦合到自适应通信以及高阶、时变连接。我们涵盖了关键的神经元模型，包括Hindmarsh-Rose模型、随机霍奇金-赫胥黎模型、谢尔曼模型和光敏菲茨休-纳古莫模型。通过研究各种同步状态的出现和稳定性，本综述突出了它们在神经系统中的重要性，并指出了这个快速发展领域未来研究的方向。
