The minimum transition hypothesis for intermittent hierarchical motor control

Frontiers in Computational Neuroscience

Amir eKarniel¹ 

[1] Ben­-Gurion University;

关键词: Spinal Cord;    motor control;    blind source separation;    Intermittent control;    muscle synergies;   

DOI  :  10.3389/fncom.2013.00012

来源: DOAJ

【 摘 要 】

In intermittent control, instead of continuously calculating the control signal, the controller occasionally changes this signal at certain sparse points in time. The control law may include feedback, adaptation, optimization, or any other control strategies. When, where, and how does the brain employ intermittency as it controls movement?These are open questions in motor neuroscience.Evidence for intermittency in human motor control has been repeatedly observed in the neural control of movement literature. Moreover, some researchers have provided theoretical models to address intermittency.Even so, the vast majority of current models, and I would dare to say the dogma in most of the current motor neuroscience literature involves continuous control.In this paper, I focus on an area in which intermittent control has not yet been thoroughly considered, the structure of muscle synergies.A synergy in the muscle space is a group of muscles activated together by a single neural command.Under the assumption that the motor control is intermittent, I present the minimum transition hypothesis and its predictions with regards to the structure of muscle synergies. The minimum transitions hypothesis (MTH) asserts that the purpose of synergies is to minimize the effort of the higher level in the hierarchy by minimizing the number of transitions in an intermittent control signal.The implications of the MTH are not only for the structure of the muscle synergies but also to the intermittent and hierarchical nature of the motor system, with various predictions as to the process of skill learning, and important implications to the design of brain machine interfaces and human robot interaction.

【 授权许可】

Unknown