A latent low-dimensional common input drives a pool of motor neurons: a probabilistic latent state-space model
A latent low-dimensional common input drives a pool of motor neurons: a probabilistic latent state-space model
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DOI：
10.1152/jn.00274.2017
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发表时间：
2017-10-01
影响因子：
2.5
通讯作者：
Enoka, Roger M.
中科院分区：
文献类型：
--
作者：
Feeney, Daniel F.;Meyer, Francois G.;Enoka, Roger M.
运动神经元似乎是由一个共同的输入信号激活的，该信号调节运动核中所有神经元的放电活动。事实证明，神经生理学家很难量化常见输入信号的变异性，但这种信号的表征可能会提高我们对激活信号在运动任务中如何变化的理解。现代量化运动神经元共同输入的方法依赖于将离散动作电位编译成连续时间序列，假设运动池充当线性滤波器，并要求信号具有足够的持续时间以进行频率分析。我们引入了一种空间状态模型，其中运动神经元的放电活动被建模为非均匀泊松过程，并提出了一种量化代表运动神经元接收到的共同输入的抽象潜在轨迹的方法。该方法还近似了公共输入信号中突触噪声的变化。该模型使用四个数据集进行验证：模拟 120 个运动单位、一对带有提供抑制反馈的 Renshaw 细胞的集成和激发神经元、10 个集成和激发神经元的放电活动，以及等长随意收缩期间同时活动的运动单位的放电时间。模拟表明，潜在状态空间模型能够量化所有四种条件下公共输入信号的轨迹和变异性。与表征公共输入的累积尖峰序列方法相比，状态空间方法对公共输入电流的细节更敏感，并且受信号持续时间的影响较小。状态空间方法似乎能够检测不同条件下公共输入信号的相当适度的变化。新的和值得注意的我们提出了一种状态空间模型，它明确地描绘了发送到运动神经元的公共输入信号以及突触信号传输中固有的生理噪声。这是确定性状态空间模型首次应用来表示自主收缩期间运动单位的放电特征。
Motor neurons appear to be activated with a common input signal that modulates the discharge activity of all neurons in the motor nucleus. It has proven difficult for neurophysiologists to quantify the variability in a common input signal, but characterization of such a signal may improve our understanding of how the activation signal varies across motor tasks. Contemporary methods of quantifying the common input to motor neurons rely on compiling discrete action potentials into continuous time series, assuming the motor pool acts as a linear filter, and requiring signals to be of sufficient duration for frequency analysis. We introduce a space-state model in which the discharge activity of motor neurons is modeled as inhomogeneous Poisson processes and propose a method to quantify an abstract latent trajectory that represents the common input received by motor neurons. The approach also approximates the variation in synaptic noise in the common input signal. The model is validated with four data sets: a simulation of 120 motor units, a pair of integrate-and-fire neurons with a Renshaw cell providing inhibitory feedback, the discharge activity of 10 integrate-and-fire neurons, and the discharge times of concurrently active motor units during an isometric voluntary contraction. The simulations revealed that a latent state-space model is able to quantify the trajectory and variability of the common input signal across all four conditions. When compared with the cumulative spike train method of characterizing common input, the state-space approach was more sensitive to the details of the common input current and was less influenced by the duration of the signal. The state- space approach appears to be capable of detecting rather modest changes in common input signals across conditions.NEW & NOTEWORTHY We propose a state-space model that explicitly delineates a common input signal sent to motor neurons and the physiological noise inherent in synaptic signal transmission. This is the first application of a deterministic state-space model to represent the discharge characteristics of motor units during voluntary contractions.