Martin Valère, Reimann Hendrik, Schöner Gregor
Institute for Neural Computation, Ruhr-University, Bochum, Germany.
Bern University of Applied Sciences, Länggasse 85, 3052, Zollikofen, Switzerland.
Biol Cybern. 2019 Jun;113(3):293-307. doi: 10.1007/s00422-019-00794-w. Epub 2019 Feb 15.
In many situations, the human movement system has more degrees of freedom than needed to achieve a given movement task. Martin et al. (Neural Comput 21(5):1371-1414, 2009) accounted for signatures of such redundancy like self-motion and motor equivalence in a process model in which a neural oscillator generated timed end-effector virtual trajectories that a neural dynamics transformed into joint virtual trajectories while decoupling task-relevant and task-irrelevant combinations of joint angles. Neural control of muscle activation and the biomechanical dynamics of the arm were taken into account. The model did not address the main signature of redundancy, however, the UCM structure of variance: Many experimental studies have shown that across repetitions, variance of joint configuration trajectories is structured. Combinations of joint angles that affect task variables (lying in the uncontrolled manifold, UCM) are much more variable than combinations of joint angles that do not. This finding has been robust across movement systems, age, and tasks and is often preserved in clinical populations as well. Here, we provide an account for the UCM structure of variance by adding four types of noise sources to the model of Martin et al. (Neural Comput 21(5):1371-1414, 2009). Comparing the model to human pointing movements and systematically examining the role of each noise source and mechanism, we identify three causes of the UCM effect, all of which, we argue, contribute: (1) the decoupling of motor commands across the task-relevant and task-irrelevant subspaces together with "neural" noise at the level of these motor commands; (2) "muscle noise" combined with imperfect control of the limb; (3) back-coupling of sensed joint configurations into the motor commands which then yield to the sensed joint configuration within the UCM.
在许多情况下，人体运动系统具有比完成给定运动任务所需更多的自由度。马丁等人（《神经计算》，2009年，第21卷第5期：1371 - 1414页）在一个过程模型中解释了这种冗余的特征，如自我运动和运动等效性。在该模型中，神经振荡器生成定时的末端执行器虚拟轨迹，神经动力学将其转换为关节虚拟轨迹，同时解耦关节角度的任务相关和任务无关组合。该模型考虑了肌肉激活的神经控制和手臂的生物力学动力学。然而，该模型没有解决冗余的主要特征，即方差的非控制流形（UCM）结构：许多实验研究表明，在多次重复中，关节配置轨迹的方差是有结构的。影响任务变量的关节角度组合（位于非控制流形内）比不影响任务变量的关节角度组合变化更大。这一发现适用于各种运动系统、年龄和任务，并且在临床人群中也常常存在。在这里，我们通过在马丁等人（《神经计算》，2009年，第21卷第5期：1371 - 1414页）的模型中添加四种类型的噪声源，来解释方差的UCM结构。将该模型与人类指向运动进行比较，并系统地研究每个噪声源和机制的作用，我们确定了UCM效应的三个原因，我们认为所有这些原因都有贡献：（1）运动指令在任务相关和任务无关子空间之间的解耦以及这些运动指令层面的“神经”噪声；（2）“肌肉噪声”与肢体控制不完善相结合；（3）感知到的关节配置反向耦合到运动指令中，然后在UCM内顺应感知到的关节配置。
