论文标题

推断耦合振荡器的连通性,并通过滞后时间分析预测其过渡到同步

Inferring the connectivity of coupled oscillators and anticipating their transition to synchrony through lag-time analysis

论文作者

Leyva, Inmaculada, Masoller, Cristina

论文摘要

同步现象本质上是普遍存在的。在耦合振荡器的集合中,爆炸性同步是一种特定的过渡到相位同步,随着耦合强度的增加,它是一阶的。在几个天然系统中已经观察到爆炸性的统一化,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库拉莫托模型,描述了耦合相振荡器的集合。在这里,我们计算双变量的相似性度量(交叉相关,$ρ_{ij} $以及相位锁定值,PLV $ _ {ij} $之间(即,最大值$ρ_{ij}(τ)$或plv $ _ {ij}(τ)$)。我们发现,随着向同步的过渡,滞后时间分布的变化提供了对同步过渡(逐渐或爆炸性)的较早警告。从罗斯勒样电子混沌振荡器中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,因为滞后时间在质量上表现出质量相似的行为,而偶联强度的增加,如库拉莫托振荡器所示。我们还分析了振荡器对之间的滞后时间与它们之间的直接连接之间的统计关系。我们发现,根据耦合的强度,滞后可以为网络连接提供信息。

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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