Loeb G E, Brown I E, Cheng E J
MRC Group in Sensory-Motor Neuroscience, Queen's University, Kingston, ON, Canada.
Exp Brain Res. 1999 May;126(1):1-18. doi: 10.1007/s002210050712.
Successful performance of a sensorimotor task arises from the interaction of descending commands from the brain with the intrinsic properties of the lower levels of the sensorimotor system, including the dynamic mechanical properties of muscle, the natural coordinates of somatosensory receptors, the interneuronal circuitry of the spinal cord, and computational noise in these elements. Engineering models of biological motor control often oversimplify or even ignore these lower levels because they appear to complicate an already difficult problem. We modeled three highly simplified control systems that reflect the essential attributes of the lower levels in three tasks: acquiring a target in the face of random torque-pulse perturbations, optimizing fusimotor gain for the same perturbations, and minimizing postural error versus energy consumption during low- versus high-frequency perturbations. The emergent properties of the lower levels maintained stability in the face of feedback delays, resolved redundancy in over-complete systems, and helped to estimate loads and respond to perturbations. We suggest a general hierarchical approach to modeling sensorimotor systems, which better reflects the real control problem faced by the brain, as a first step toward identifying the actual neurocomputational steps and their anatomical partitioning in the brain.
感觉运动任务的成功执行源于大脑下行指令与感觉运动系统较低层级的内在属性之间的相互作用，这些内在属性包括肌肉的动态力学特性、体感感受器的自然坐标、脊髓的中间神经元回路以及这些元件中的计算噪声。生物运动控制的工程模型常常过度简化甚至忽略这些较低层级，因为它们似乎会使一个原本就困难的问题变得更加复杂。我们对三个高度简化的控制系统进行了建模，这些系统反映了三个任务中较低层级的基本属性：在面对随机扭矩脉冲扰动时获取目标、针对相同扰动优化肌梭运动增益，以及在低频与高频扰动期间最小化姿势误差与能量消耗。较低层级的涌现特性在面对反馈延迟时维持了稳定性，解决了超完备系统中的冗余问题，并有助于估计负荷并对扰动做出响应。我们提出一种用于对感觉运动系统进行建模的通用分层方法，作为朝着识别大脑中实际神经计算步骤及其解剖学划分迈出的第一步，该方法能更好地反映大脑所面临的实际控制问题。
