In sensorimotor control, sensory feedback integrates with forward models to alleviate the impacts of sensory noise and delay on state estimation. The sensorimotor integration is subject to Bayesian inference and has been formulated by the Kalman filter in computational neuroscience. However, the Kalman filter, as an artificial optimal estimator to address the abstract characteristics of spatial perception, is inadequate to present the neural computation in the cerebellum. Besides, the nonlinear neuromuscular dynamics with tightly coupled state variables also substantially impedes the implementation of Kalman filter in realistic sensorimotor systems. Here we address the sensorimotor state estimate by using the particle filter, a nonlinear Bayesian estimator that can be implemented in arbitrary dynamic systems with the neurocomputational compatibility. Particle filtering is explicitly implemented in a biophysically realistic sensorimotor model of an upper limb integrating Hill-type muscles, tendons, skeleton, and primary afferents. By involving the command noises, the constructed neuromusculoskeletal model qualitatively represents the experimental variability in center-out reaching movements. Despite the initial estimation uncertainty and sensorimotor noises, the particle filter is able to approximate the actual states in forward-reaching movements. Furthermore, the simulated hand-position estimate is consistent with the experimental results, in the presence of forward model errors, neural noises, and sensory delays. The particle filter is demonstrated to effectively implement the Bayesian state estimation in biophysically realistic sensorimotor systems and provide better compatibility with neuronal computation than the Kalman filter.

中文翻译：

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带粒子滤波的感觉运动系统的贝叶斯状态估计。

在感觉运动控制中，感觉反馈与前向模型集成在一起，以减轻感觉噪声和延迟对状态估计的影响。感觉运动积分受贝叶斯推断，并由计算神经科学中的卡尔曼滤波器确定。然而，卡尔曼滤波器作为解决空间感知抽象特征的人工最佳估计器，不足以呈现小脑的神经计算。此外，具有紧密耦合状态变量的非线性神经肌肉动力学也极大地阻碍了卡尔曼滤波器在现实的感觉运动系统中的实现。在这里，我们通过使用粒子滤波器处理感觉运动状态估计，非线性贝叶斯估计器，可以在具有神经计算兼容性的任意动态系统中实现。微粒过滤在整合了希尔型肌肉，肌腱，骨骼和初级传入神经的上肢的生物物理现实感觉运动模型中明确实现。通过涉及命令噪声，所构建的神经肌肉骨骼模型定性地代表了中心向外到达运动的实验变异性。尽管初步估计存在不确定性和感觉运动噪声，但粒子过滤器仍能够近似向前移动中的实际状态。此外，在存在正向模型误差，神经噪声和感觉延迟的情况下，模拟的手部位置估计与实验结果一致。