通过滞后时间分析推断耦合振子的连通性并预测它们向同步的过渡
Inferring the connectivity of coupled oscillators and anticipating their transition to synchrony through lag-time analysis
摘要：
同步现象在自然界中普遍存在。在耦合振子的系综中，爆发同步是一种特殊类型的相位同步，随着耦合强度的增加，这种相位同步是一阶的。爆炸性同步已经在几个自然系统中观察到，最近的证据表明，它也可能发生在大脑中。研究这种现象的自然系统是描述耦合相位振荡器系综的仓本模型。这里，我们计算振荡器对的相位$ϕ_i(t)$和$ϕ_j(t)$之间的双变量相似性度量（互相关$ρ_{ij}$和相位锁定值PLV $_{ij}$），并将它们之间的滞后时间确定为时移$τ_{ij}$，其给出最大相似性（即，$ρ_{ij}(τ)$或PLV $_{ij}(τ)$的最大值）。我们发现，作为过渡到同步接近，滞后时间分布的变化提供了一个早期的警告同步过渡（无论是渐进的或爆炸性的）。从Rossler类电子混沌振荡器记录的实验数据的分析表明，这些发现不仅限于相位振荡器，因为滞后时间随着耦合强度的增加而显示出定性的相似行为，如仓本振荡器。我们还分析了振子对之间的滞后时间之间的统计关系和它们之间存在的直接联系。我们发现，根据耦合的强度，滞后可以是网络连接的信息。
英文摘要：
The synchronization phenomenon is ubiquitous in nature. In ensembles of coupled oscillators, explosive synchronization is a particular type of transition to phase synchrony that is first-order as the coupling strength increases. Explosive sychronization has been observed in several natural systems, and recent evidence suggests that it might also occur in the brain. A natural system to study this phenomenon is the Kuramoto model that describes an ensemble of coupled phase oscillators. Here we calculate bi-variate similarity measures (the cross-correlation, $ρ_{ij}$, and the phase locking value, PLV$_{ij}$) between the phases, $ϕ_i(t)$ and $ϕ_j(t)$, of pairs of oscillators and determine the lag time between them as the time-shift, $τ_{ij}$, which gives maximum similarity (i.e., the maximum of $ρ_{ij}(τ)$ or PLV$_{ij}(τ)$). We find that, as the transition to synchrony is approached, changes in the distribution of lag times provide an earlier warning of the synchronization transition (either gradual or explosive). The analysis of experimental data, recorded from Rossler-like electronic chaotic oscillators, suggests that these findings are not limited to phase oscillators, as the lag times display qualitatively similar behavior with increasing coupling strength, as in the Kuramoto oscillators. We also analyze the statistical relationship between the lag times between pairs of oscillators and the existence of a direct connection between them. We find that depending on the strength of the coupling, the lags can be informative of the network connectivity.
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