Avila Bryant, Augusto Pedro, Zimmer Manuel, Serafino Matteo, Makse Hernán A
Levich Institute, Physics Department, City College of New York, New York, NY, USA.
Vienna Biocenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria.
ArXiv. 2024 May 3:arXiv:2305.19367v2.
Capturing how the connectome structure gives rise to its neuron functionality remains unclear. It is through fiber symmetries found in its neuronal connectivity that synchronization of a group of neurons can be determined. To understand these we investigate graph symmetries and search for such in the symmetrized versions of the forward and backward locomotive sub-networks of the worm neuron network. The use of ordinarily differential equations simulations admissible to these graphs are used to validate the predictions of these fiber symmetries and are compared to the more restrictive orbit symmetries. Additionally fibration symmetries are used to decompose these graphs into their fundamental building blocks which reveal units formed by nested loops or multilayered fibers. It is found that fiber symmetries of the connectome can accurately predict neuronal synchronization even under not idealized connectivity as long as the dynamics are within stable regimes of simulations.
目前尚不清楚连接体结构如何产生其神经元功能。正是通过其神经元连接中发现的纤维对称性,才能确定一组神经元的同步性。为了理解这些,我们研究图对称性,并在蠕虫神经元网络的前向和后向运动子网的对称版本中寻找此类对称性。使用适用于这些图的常微分方程模拟来验证这些纤维对称性的预测,并与更严格的轨道对称性进行比较。此外,纤维化对称性用于将这些图分解为其基本构建块,这些构建块揭示了由嵌套环或多层纤维形成的单元。研究发现,只要动力学处于模拟的稳定范围内,即使在非理想化的连接情况下,连接体的纤维对称性也能准确预测神经元同步。