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The Poincaré map of randomly perturbed periodic motion

机译:随机扰动的周期性运动的庞加莱图

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摘要

A system of autonomous differential equations with a stable limit cycle and perturbed by small white noise is analyzed in this work. In the vicinity of the limit cycle of the unperturbed deterministic system, we define, construct, and analyze the Poincaré map of the randomly perturbed periodic motion. We show that the time of the first exit from a small neighborhood of the fixed point of the map, which corresponds to the unperturbed periodic orbit, is well approximated by the geometric distribution. The parameter of the geometric distribution tends to zero together with the noise intensity. Therefore, our result can be interpreted as an estimate of the stability of periodic motion to random perturbations. In addition, we show that the geometric distribution of the first exit times translates into statistical properties of solutions of important differential equation models in applications. To this end, we demonstrate three distinct examples from mathematical neuroscience featuring complex oscillatory patterns characterized by the geometric distribution. We show that in each of these models the statistical properties of emerging oscillations are fully explained by the general properties of randomly perturbed periodic motions identified in this paper.
机译:在这项工作中,分析了一个具有稳定极限环并受小白噪声干扰的自治微分方程系统。在无扰动确定性系统的极限环附近,我们定义,构造和分析随机扰动的周期性运动的庞加莱图。我们显示,从地图固定点的一个小邻域第一次退出的时间(对应于未扰动的周期性轨道)可以通过几何分布很好地估算出来。几何分布的参数与噪声强度一起趋于零。因此,我们的结果可以解释为对随机扰动的周期性运动稳定性的估计。此外,我们证明了首次退出时间的几何分布转化为应用中重要的微分方程模型的解的统计性质。为此,我们展示了数学神经科学的三个不同示例,这些示例具有以几何分布为特征的复杂振荡模式。我们表明,在每个模型中,新兴振荡的统计特性都可以由本文确定的随机扰动的周期性运动的一般特性完全解释。

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