在库拉莫托网络中,呼吸和切换旋状态与更高模式合
Maxim I Bolotov1, Vyacheslav O Munyayev1, Lev A Smirnov1
1Department of Control Theory, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, Nizhny Novgorod, 603022, Russia.
Physical review. E
|June 22, 2024
概括
研究人员探索了如何在振荡器网络中破坏旋状态,发现呼吸和切换旋状态. 这些新的动态模式在合振荡器系统中,包括神经网络中,是普遍且强大的.
科学领域:
- 复杂的系统复杂的系统.
- 非线性动力学是一种非线性动力学.
- 网络科学 网络科学
背景情况:
- 由两个连贯的集群和一个孤独的振荡器特征的旋状态,以前在排斥的库拉莫托网络中被识别出来.
- 在具有更高模式合波的网络中,这些状态的流行是一个令人惊的发现.
研究的目的:
- 为了研究破坏旋状态的机制.
- 为了识别和描述新的动态模式:呼吸和切换旋状态.
- 分析这些新型状态在振荡器网络中的稳定性和普遍性.
主要方法:
- 在Kuramoto-Sakaguchi网络中分析研究圆环状态,具有二维振荡器和惯性.
- 检查第二合波对状态稳定性的影响.
- 识别导致呼吸和切换旋状态的分叉场景.
主要成果:
- 确定了两个分叉场景,产生呼吸和切换旋状态.
- 这些新状态及其混合体在广泛的合范围内普遍存在.
- 发现的状态显示出对显著的内在频率脱调的强度.
结论:
- 呼吸和切换循环状态代表了振荡器网络中的新动态模式.
- 这些状态是强大而广泛的,超出了库拉莫托网络最初的发现.
- 这项研究表明,在各种物理和生物系统中存在潜在的表现,例如结合的甲型神经元.
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