Neural representation of muscle dynamics in voluntary movement control

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• 作者：Christopher J. Hasson (1)
  
• 关键词：Virtual limb ; Muscle mechanics ; Motor learning ; Voluntary movement control ; Muscle dynamics ; Nervous system ; Neural representation
• 刊名：Experimental Brain Research
• 出版年：2014
• 出版时间：July 2014
• 年：2014
• 卷：232
• 期：7
• 页码：2105-2119
• 全文大小：
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• 作者单位：Christopher J. Hasson (1)
  
  1. Department of Physical Therapy, Movement and Rehabilitation Sciences, Neuromotor Systems Laboratory, Northeastern University, 360 Huntington Avenue, 301 Robinson Hall, Boston, MA, 02115-5005, USA
  
• ISSN：1432-1106

文摘

Several theories of motor control posit that the nervous system has access to a neural representation of muscle dynamics. Yet, this has not been tested experimentally. Should such a representation exist, it was hypothesized that subjects who learned to control a virtual limb using virtual muscles would improve performance faster and show greater generalization than those who learned with a less dynamically complex virtual force generator. Healthy adults practiced using their biceps brachii activity to move a myoelectrically controlled virtual limb from rest to a standard target position with maximum speed and accuracy. Throughout practice, generalization was assessed with untrained target trials and sensitivity to actuator dynamics was probed by unexpected actuator model switches. In a muscle model subject group (n?=?10), the biceps electromyographic signal activated a virtual muscle that pulled on the virtual limb with a force governed by muscle dynamics, defined by a nonlinear force–length–velocity relation and series elastic stiffness. A force generator group (n?=?10) performed the same task, but the actuation force was a linear function of the biceps activation signal. Both groups made significant errors with unexpected actuator dynamics switches, supporting task sensitivity to actuator dynamics. The muscle model group improved performance as fast as the force generator group and showed greater generalization in early practice, despite using an actuator with more complex dynamics. These results are consistent with a preexisting neural representation of muscle dynamics, which may have offset any?learning challenges associated with the more dynamically complex virtual muscle model.