主动非马尔可夫振荡模型
Modelling Active Non-Markovian Oscillations
摘要：
对有源系统的噪声振荡进行建模是当前物理学和生物学中的挑战之一。由于这些过程的物理机制往往很难确定，我们提出了一个线性随机模型驱动的非马尔可夫随机噪声，能够产生自我维持的周期振荡。我们得到的模型的最相关的动力学和热力学性质的分析预测。这个极小模型准确地描述了牛蛙球囊中毛束的双稳态振荡运动。基于这些数据并与之一致，我们估计维持这种主动振荡所需的功率为每个振荡周期100 $k_B T$的量级。
英文摘要：
Modelling noisy oscillations of active systems is one of the current challenges in physics and biology. Because the physical mechanisms of such processes are often difficult to identify, we propose a linear stochastic model driven by a non-Markovian bistable noise that is capable of generating self-sustained periodic oscillation. We derive analytical predictions for most relevant dynamical and thermodynamic properties of the model. This minimal model turns out to describe accurately bistable-like oscillatory motion of hair bundles in bullfrog sacculus, extracted from experimental data. Based on and in agreement with these data, we estimate the power required to sustain such active oscillations to be of the order of one hundred $k_B T$ per oscillation cycle.
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