Schweighofer N, Arbib M A, Kawato M
Centre for Neural Engineering, University of Southern California, Los Angeles 90089-2520, USA.
Eur J Neurosci. 1998 Jan;10(1):86-94. doi: 10.1046/j.1460-9568.1998.00006.x.
This study focuses on the role of the motor cortex, the spinal cord and the cerebellum in the dynamics stage of the control of arm movement. Currently, two classes of models have been proposed for the neural control of movements, namely the virtual trajectory control hypothesis and the acquisition of internal models of the motor apparatus hypothesis. In the present study, we expand the virtual trajectory model to whole arm reaching movements. This expanded model accurately reproduced slow movements, but faster reaching movements deviated significantly from the planned trajectories, indicating that for fast movements, this model was not sufficient. These results led us to propose a new distributed functional model consistent with behavioural, anatomical and neurophysiological data, which takes into account arm muscles, spinal cord, motor cortex and cerebellum and is consistent with the view that the central nervous system acquires a distributed inverse dynamics model of the arm. Previous studies indicated that the cerebellum compensates for the interaction forces that arise during reaching movements. We show here how the cerebellum may increase the accuracy of reaching movements by compensating for the interaction torques by learning a portion of an inverse dynamics model that refines a basic inverse model in the motor cortex and spinal cord.
本研究聚焦于运动皮层、脊髓和小脑在手臂运动控制动态阶段所起的作用。目前，针对运动的神经控制已提出两类模型，即虚拟轨迹控制假说和运动装置内部模型习得假说。在本研究中，我们将虚拟轨迹模型扩展至整个手臂的伸展运动。这个扩展模型能够精确再现缓慢运动，但快速伸展运动却显著偏离计划轨迹，这表明对于快速运动而言，该模型并不充分。这些结果促使我们提出一个与行为学、解剖学和神经生理学数据相一致的新的分布式功能模型，该模型考虑了手臂肌肉、脊髓、运动皮层和小脑，并且与中枢神经系统获取手臂分布式逆动力学模型的观点相符。先前的研究表明，小脑可补偿伸展运动过程中产生的相互作用力。我们在此展示了小脑如何通过学习逆动力学模型的一部分来补偿相互作用扭矩，从而提高伸展运动的准确性，该部分逆动力学模型完善了运动皮层和脊髓中的基本逆模型。
