论文标题
竞争竞争资源的库拉莫托振荡器的同步
Synchronization of coupled Kuramoto oscillators competing for resources
论文作者
论文摘要
振荡器的种群在整个自然界中存在。如果允许相互作用,通常会在此类人群中观察到同步。用于研究这种现象的范式模型是库拉莫托模型。但是,考虑到真正的振荡很少是能量函数的等于等方的,因此可以通过允许固有频率随着某些动态资源供应的函数而变化来扩展模型是很自然的。不仅考虑了动态资源的供应,但是在各种生物系统中,对\ emph {共享}资源供应的竞争至关重要。例如,在神经元系统中,资源竞争可以通过fMRI研究神经活动。可以合理地期望这种动态资源分配应该对大脑的同步行为产生后果。本文提出了一种修改的库拉莫托动力学,其中包括其他动力学术语,该术语为库拉莫托振荡器种群中的资源竞争提供了相对简单的模型。我们设计了一个Mutlilayer系统,该系统突出了竞争动态的影响,我们表明,在设计系统中,可以在两个振荡器的同步状态之间出现相关性,这些振荡器的同步状态共享没有相耦合边缘。鉴于实际系统中功能和结构连接度量之间经常观察到的差异,这些相关性很有趣。然后在此提出的模型表明,某些观察到的差异可以通过大脑根据需求将资源动态分配给不同区域的方式来解释。如果是真的,这样的模型提供了一个理论框架,用于分析结构和功能度量之间的差异,并可能将动态资源分配牵涉到神经计算过程中不可或缺的一部分。
Populations of oscillators are present throughout nature. Very often synchronization is observed in such populations if they are allowed to interact. A paradigmatic model for the study of such phenomena has been the Kuramoto model. However, considering real oscillations are rarely isochronous as a function of energy, it is natural to extend the model by allowing the natural frequencies to vary as a function of some dynamical resource supply. Beyond just accounting for a dynamical supply of resources, however, competition over a \emph{shared} resource supply is important in a variety of biological systems. In neuronal systems, for example, resource competition enables the study of neural activity via fMRI. It is reasonable to expect that this dynamical resource allocation should have consequences for the synchronization behavior of the brain. This paper presents a modified Kuramoto dynamics which includes additional dynamical terms that provide a relatively simple model of resource competition among populations of Kuramoto oscillators. We design a mutlilayer system which highlights the impact of the competition dynamics, and we show that in this designed system, correlations can arise between the synchronization states of two populations of oscillators which share no phase-coupling edges. These correlations are interesting in light of the often observed variance between functional and structural connectivity measures in real systems. The model presented here then suggests that some of the observed discrepancy may be explained by the way in which the brain dynamically allocates resources to different regions according to demand. If true, models such as this one provide a theoretical framework for analyzing the differences between structural and functional measures, and possibly implicate dynamical resource allocation as an integral part of the neural computation process.