Rulkov神经网络簇发放电动力学状态的符号分析
Symbolic analysis of bursting dynamical regimes of Rulkov neural networks
AI总结：
该研究针对Rulkov映射建模的神经元集群，采用序数分析这一符号时间序列方法，表征Watts-Strogatz小世界耦合网络的簇发序列，发现符号概率可区分依赖耦合强度与拓扑的动力学状态，且可通过光栅图可视化其时空特性。
摘要：
由Rulkov映射建模的神经元展现出多种动力学状态，包括紧张性峰电位和混沌簇发。本文研究了一组以Watts-Strogatz小世界拓扑结构耦合的簇发神经元。我们采用时间序列分析的符号方法——序数分析（可检测非线性时间相关性）来表征簇发序列。研究表明，不同符号的概率可区分依赖于耦合强度和网络拓扑的不同动力学状态。这些状态具有不同的时空特性，可通过光栅图进行可视化。
英文摘要：
Neurons modeled by the Rulkov map display a variety of dynamic regimes that include tonic spikes and chaotic bursting. Here we study an ensemble of bursting neurons coupled with the Watts-Strogatz small-world topology. We characterize the sequences of bursts using the symbolic method of time-series analysis known as ordinal analysis, which detects nonlinear temporal correlations. We show that the probabilities of the different symbols distinguish different dynamical regimes, which depend on the coupling strength and the network topology. These regimes have different spatio-temporal properties that can be visualized with raster plots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
