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随机振荡器的渐近相。

Asymptotic phase for stochastic oscillators.

机构信息

Bernstein Center for Computational Neuroscience, Humboldt University, 10115 Berlin, Germany and Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, Ohio 44106, USA.

Bernstein Center for Computational Neuroscience and Department of Physics, Humboldt University, 10115 Berlin, Germany.

出版信息

Phys Rev Lett. 2014 Dec 19;113(25):254101. doi: 10.1103/PhysRevLett.113.254101. Epub 2014 Dec 15.

Abstract

Oscillations and noise are ubiquitous in physical and biological systems. When oscillations arise from a deterministic limit cycle, entrainment and synchronization may be analyzed in terms of the asymptotic phase function. In the presence of noise, the asymptotic phase is no longer well defined. We introduce a new definition of asymptotic phase in terms of the slowest decaying modes of the Kolmogorov backward operator. Our stochastic asymptotic phase is well defined for noisy oscillators, even when the oscillations are noise dependent. It reduces to the classical asymptotic phase in the limit of vanishing noise. The phase can be obtained either by solving an eigenvalue problem, or by empirical observation of an oscillating density's approach to its steady state.

摘要

在物理和生物系统中,波动和噪声无处不在。当波动源于确定性的极限环时,可以根据渐近相位函数来分析锁相和同步。在存在噪声的情况下,渐近相位不再是明确定义的。我们引入了一个新的渐近相位定义,它基于柯尔莫哥洛夫后向算子的最慢衰减模式。我们的随机渐近相位对于有噪声的振荡器是明确定义的,即使振荡器依赖于噪声。当噪声趋于零时,它会退化为经典的渐近相位。相位可以通过求解特征值问题,或者通过观察波动密度趋近其稳态的经验来获得。

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