Dynamic Modulation of a Learned Motor Skill for Its Recruitment - Min, Kyuengbo; Lee, Jongho; Kakei, Shinji (Frontiers in Computational Neuroscience, 2020)
Humans learn a motor skill (MS) through practice and experience and then may retain it for its recruitment, which is effective in rapid response for novel contexts. For a MS to be recruited for novel contexts, its recruitment range must be extended. In addressing this issue, we hypothesized that a MS is dynamically modulated according to feedback context to expand its recruitment range into novel contexts, which do not involve the learning of a MS. The following two sub-issues are considered. We previously demonstrated that the learned MS could be recruited in novel contexts through its modulation, which is driven by dynamically regulating the synergistic redundancy between muscles according to feedback context. However, this modulation is trained in the dynamics under the MS learning context. Learning a MS in a specific condition naturally causes movement deviation from the desired state when the MS is executed in a novel context. We hypothesized that this deviation can be reduced with the additional modulation of a MS, which tunes the MS-produced muscle activities with using the feedback gain signals driven by the deviation from the desired state. Based on this hypothesis, we newly propose the feedback gain signals-driven tuning model of a learned MS for its robust recruitment. This model is based on the neurophysiological architecture in the cortico-basal ganglia circuit, in which a MS is plausibly retained as it was learned and is then recruited by tuning its muscle control signals according to feedback context. In this study, through computational simulation, we show that the proposed model may be used to neurophysiologically describe the recruitment of a learned MS in novel contexts.