Synergy quality assessment of muscle modules for determining learning performance using a realistic musculoskeletal model - Fukunishi, Akito; Kutsuzawa, Kyo; Owaki, Dai; Hayashibe, Mitsuhiro (Frontiers in Computational Neuroscience, 2024)
How our central nervous system efficiently controls our complex musculoskeletal system is still debated. The muscle synergy hypothesis is proposed to simplify this complex system by assuming the existence of functional neural modules that coordinate several muscles. Modularity based in muscle synergies can facilitate motor learning without compromising task performance.However, the effectiveness of modularity in motor control remains debated, This ambiguity can, in part, stems from overlooking that the performance of modularity depends on the mechanical aspects of modules of interest, such as the torque the modules exert. To address this issue, this paper introduces two criteria to evaluate the quality of module sets based on commonly used performance metrics in motor learning studies: the accuracy of torque production and learning speed. One evaluates the regularity in the direction of mechanical torque of the modules exert, while the other evaluates the evenness of its magnitude. For verification of our criteria, we simulated motor learning of torque production tasks in a realistic musculoskeletal system of the upper arm using feed-forward neural networks while changing the control conditions. Overall, the criteria can be applied to a complex musculoskeletal model that contains the passive force element of the muscle and succeeds in explaining the tendency of the learning performance in various control conditions, supporting the effectiveness of the criteria. Although the criteria were originally conceived for an error-based learning scheme, the approach to pursue which set of modules is better for motor control can have significant implications in other studies of modularity in general.