论文标题
尽管有噪音,时钟可以一起滴答吗?随机模拟和分析
Can the clocks tick together despite the noise? Stochastic simulations and analysis
论文作者
论文摘要
上核(SCN),也称为昼夜节律大师时钟,由大量振荡器神经元组成。这些神经元共同产生一个连贯的信号,可驱动人体的昼夜节律。尽管存在广泛的环境挑战,例如光周期的波动,但细胞间通信的哪些特性允许这些神经元同步?为了回答这个问题,我们提出了以标准高斯噪声为古德温振荡器的全球耦合神经元的平均场所描述。只要所有神经元的初始条件都是独立的,并且分布相同,则任何有限数量的神经元都变得独立,并且在平均场极限中具有相同的概率分布,这一现象称为混乱的传播。此概率分布是对Vlasov-Fokker-Planck类型方程的解决方案,可以从随机粒子模型获得。我们使用宏观描述研究,外部噪声与细胞间耦合之间的相互作用如何影响集体节奏的动力学,并且我们提供了由噪声引起的过渡产生的分叉的数值描述。我们的数值模拟显示在低噪声强度下噪声引起的节奏产生,而在高噪声设置中,SCN时钟是心律不齐的。值得注意的是,耦合在低噪声强度下引起类似共振的行为,即使在存在噪声的情况下,变化的耦合强度也会导致周期锁定和差异。
The suprachiasmatic nucleus (SCN), also known as the circadian master clock, consists of a large population of oscillator neurons. Together, these neurons produce a coherent signal that drives the body's circadian rhythms. What properties of the cell-to-cell communication allow the synchronization of these neurons, despite a wide range of environmental challenges such as fluctuations in photoperiods? To answer that question, we present a mean-field description of globally coupled neurons modeled as Goodwin oscillators with standard Gaussian noise. Provided that the initial conditions of all neurons are independent and identically distributed, any finite number of neurons becomes independent and has the same probability distribution in the mean-field limit, a phenomenon called propagation of chaos. This probability distribution is a solution to a Vlasov-Fokker-Planck type equation, which can be obtained from the stochastic particle model. We study, using the macroscopic description, how the interaction between external noise and intercellular coupling affects the dynamics of the collective rhythm, and we provide a numerical description of the bifurcations resulting from the noise-induced transitions. Our numerical simulations show a noise-induced rhythm generation at low noise intensities, while the SCN clock is arrhythmic in the high noise setting. Notably, coupling induces resonance-like behavior at low noise intensities, and varying coupling strength can cause period locking and variance dissipation even in the presence of noise.