School of Mathematical Sciences, Zhejiang University, Hangzhou, China.
Front Neural Circuits. 2024 Jan 4;17:1308629. doi: 10.3389/fncir.2023.1308629. eCollection 2023.
Topological data analysis is becoming more and more popular in recent years. It has found various applications in many different fields, for its convenience in analyzing and understanding the structure and dynamic of complex systems. We used topological data analysis to analyze the firings of a network of stochastic spiking neurons, which can be in a sub-critical, critical, or super-critical state depending on the value of the control parameter. We calculated several topological features regarding Betti curves and then analyzed the behaviors of these features, using them as inputs for machine learning to discriminate the three states of the network.
近年来，拓扑数据分析变得越来越流行。由于其在分析和理解复杂系统的结构和动态方面的便利性，它在许多不同的领域都有了各种应用。我们使用拓扑数据分析来分析随机尖峰神经元网络的放电情况，根据控制参数的值，网络可以处于亚临界、临界或超临界状态。我们计算了关于贝蒂曲线的几个拓扑特征，然后分析了这些特征的行为，将它们作为机器学习的输入来区分网络的三种状态。
