协同作用的拓学:将拓学和信息理论方法与复杂系统中高阶相互作用联系起来
Thomas F Varley1,2, Pedro A M Mediano3,4, Alice Patania1,5
1Vermont Complex Systems Institute, University of Vermont, Burlington, Vermont, United States of America.
PLoS computational biology
|November 13, 2025
概括
这项研究比较了拓数据分析和信息理论,以了解复杂的系统. 它发现数据中的更高阶协同信息,包括大脑fMRI信号,与3D空洞相关,挑战传统方法.
科学领域:
- 复杂系统科学 复杂系统科学
- 网络科学 网络科学
- 数据分析数据分析
背景情况:
- 高阶相互作用对于理解复杂系统的出现至关重要.
- 拓数据分析和多变量信息理论是识别这些相互作用的关键框架.
- 这些框架与有限的跨学科交流并行发展.
研究的目的:
- 为了比较拓数据分析和信息理论方法对更高层次的相互作用.
- 评估这些框架如何定义更高阶结构的相似之处和差异.
- 评估它们对合成数据和现实数据的应用,例如fMRI信号.
主要方法:
- 拓数据分析和多变量信息理论的比较分析.
- 适用于具有已知拓的玩具实例 (球体,圆柱体,平面,结).
- 从人类大脑中分析功能磁共振成像 (fMRI) 数据.
主要成果:
- 高级协同信息与数据中的3D空洞 (例如球体,空洞陀罗伊德) 有关.
- fMRI数据显示了协同信息与3D腔的数量/大小之间的强烈相关性.
- 像PCA这样的尺寸缩小技术无法保留更高阶的信息和拓结构.
结论:
- 高阶相互作用的统一理论可以跨越拓学和信息理论方法.
- 传统的基于多元组的方法可能会忽略关键数据特征.
- 像3D空洞这样的拓特征是复杂系统中协同信息的关键指标.
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