Avila Bryant, Augusto Pedro, Phillips David, Gili Tommaso, Zimmer Manuel, Makse Hernán A
Levich Institute and Physics Department, City College of New York, New York, NY 10031, USA.
Department of Neuroscience and Developmental Biology, University of Vienna, Vienna Biocenter (VBC), Vienna, Austria.
ArXiv. 2024 Sep 4:arXiv:2409.02682v1.
Understanding the dynamical behavior of complex systems from their underlying network architectures is a long-standing question in complexity theory. Therefore, many metrics have been devised to extract network features like motifs, centrality, and modularity measures. It has previously been proposed that network symmetries are of particular importance since they are expected to underly the synchronization of a system's units, which is ubiquitously observed in nervous system activity patterns. However, perfectly symmetrical structures are difficult to assess in noisy measurements of biological systems, like neuronal connectomes. Here, we devise a principled method to infer network symmetries from combined connectome and neuronal activity data. Using nervous system-wide population activity recordings of the backward locomotor system, we infer structures in the connectome called fibration symmetries, which can explain which group of neurons synchronize their activity. Our analysis suggests functional building blocks in the animal's motor periphery, providing new testable hypotheses on how descending interneuron circuits communicate with the motor periphery to control behavior. Our approach opens a new door to exploring the structure-function relations in other complex systems, like the nervous systems of larger animals.
从复杂系统的底层网络架构理解其动力学行为是复杂性理论中一个长期存在的问题。因此，人们设计了许多指标来提取网络特征，如基序、中心性和模块化度量。此前有人提出，网络对称性尤为重要，因为它们被认为是系统单元同步的基础，而这种同步在神经系统活动模式中普遍存在。然而，在生物系统（如神经元连接组）的噪声测量中，完美对称的结构很难评估。在这里，我们设计了一种有原则的方法，从连接组和神经元活动数据的组合中推断网络对称性。利用向后运动系统的全神经系统群体活动记录，我们推断出连接组中称为纤维化对称性的结构，它可以解释哪一组神经元同步其活动。我们的分析揭示了动物运动外周的功能构建块，为下行中间神经元回路如何与运动外周通信以控制行为提供了新的可测试假设。我们的方法为探索其他复杂系统（如大型动物的神经系统）中的结构-功能关系打开了一扇新的大门。
