通过相动力学重建推断振荡网络的连接性
Michael Rosenblum1, Arkady Pikovsky1
1Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany.
Frontiers in network physiology
|December 11, 2023
概括
本研究提出了一种方法,通过推断相位动态来从观察到的数据中重建振荡网络连接. 它涵盖了光滑振荡和点过程数据,使无定向和定向链路的表征成为可能.
科学领域:
- 神经科学是一个神经科学.
- 复杂的系统复杂的系统.
- 网络科学 网络科学
背景情况:
- 了解振荡网络中的连接性对于神经科学和物理学等领域至关重要.
- 现有的方法经常与复杂的网络结构和不同类型的数据进行斗争.
研究的目的:
- 从观察到的数据中呈现一种统一的方法,用于从观察到的数据中重建振荡网络中的非定向和定向连接.
- 为了应对不同信号类型 (平滑振荡与点过程) 和网络大小所带来的挑战.
主要方法:
- 从观察到的节点输出推断阶段动态模型.
- 从标量信号中估计真相,使用原相相变换来实现平滑的振荡.
- 使用尖端瞬间来推断点过程数据的相位动态.
- 计算非定向连接的双向和三重同步指数.
- 推断合功能来量化方向链接,并区分结构和有效连接.
主要成果:
- 使用同步索引来描述非定向连接的一种方法.
- 一种通过量化合函数来推断方向链接的技术.
- 在更大的网络中,结构和有效相连接之间的区别.
- 从点处理数据直接推断合矩阵.
结论:
- 拟议的方法为跨不同振荡数据类型的网络连接重建提供了一个强大的框架.
- 这种方法推进了复杂系统的分析,其中相互作用通过相位动力学被揭示出来.
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