JRM Vol.34 No.4 pp. 817-827
doi: 10.20965/jrm.2022.p0817


A Motor Adaptation Model Assuming Update of Internal Model in the Motor Cortex

Sho Furubayashi, Takahiro Hasegawa, and Eizo Miyashita

School of Life Science and Technology, Tokyo Institute of Technology
4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan

February 2, 2022
June 7, 2022
August 20, 2022
motion control, prediction error, neural control of movements, Hebb’s rule, visuomotor adaptation
A Motor Adaptation Model Assuming Update of Internal Model in the Motor Cortex

Flow of the adaptation model

When considering the human motor-adaptation mechanism from the perspective of the motor control theory, updating the internal model constitutes a critical component. The learning curve at each trial of motion can be explained by a state-space model; however, the model cannot reproduce the time-series data for the hand’s position, velocity, and acceleration (motion profiles). There is no internal model-updating rule for optimal feedback control, a plausible model for reproducing motion profiles. In this paper, we propose an adaptation model that incorporates an internal model-updating rule which modeled after Hebb’s rule into optimal feedback control. Also, we examine the neural substrate of the internal model. To validate the proposed adaptation model, we conducted behavioral experiments with humans that reflected changes in the internal model and reproduced the changes in the internal model as well as the motion profiles using the proposed adaptation model. In addition, we analyzed the data for a visuomotor rotation task performed by a monkey and checked for changes in the output characteristics of neurons in the motor cortex before and after adaptation. According to the above-mentioned validation and analysis results, the motor cortex constitutes the neural substrate of the internal model.

Cite this article as:
S. Furubayashi, T. Hasegawa, and E. Miyashita, “A Motor Adaptation Model Assuming Update of Internal Model in the Motor Cortex,” J. Robot. Mechatron., Vol.34, No.4, pp. 817-827, 2022.
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Last updated on Sep. 22, 2022