Fukunishi Akito, Kutsuzawa Kyo, Owaki Dai, Hayashibe Mitsuhiro
Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan.
Front Comput Neurosci. 2024 May 30;18:1355855. doi: 10.3389/fncom.2024.1355855. eCollection 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 on 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, stem 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 study 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 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. We found that the proposed criteria successfully explain the tendency of learning performance in various control conditions. These result suggest that regularity in the direction of and evenness in magnitude of mechanical torque of utilized modules are significant factor for determining learning performance. 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.
我们的中枢神经系统如何有效地控制复杂的肌肉骨骼系统仍存在争议。肌肉协同假说被提出来简化这个复杂系统，该假说假定存在协调多块肌肉的功能性神经模块。基于肌肉协同的模块化能够促进运动学习，同时又不影响任务表现。然而，模块化在运动控制中的有效性仍存在争议。这种模糊性部分源于忽视了模块化的表现取决于相关模块的力学方面，比如模块所施加的扭矩。为解决这个问题，本研究引入了两个标准，基于运动学习研究中常用的性能指标来评估模块集的质量：扭矩产生的准确性和学习速度。一个标准评估模块所施加的机械扭矩方向的规律性，另一个评估其大小的均匀性。为验证我们的标准，我们在改变控制条件的同时，使用前馈神经网络在上臂的真实肌肉骨骼系统中模拟扭矩产生任务的运动学习。我们发现所提出的标准成功解释了各种控制条件下的学习表现趋势。这些结果表明，所使用模块的机械扭矩方向的规律性和大小的均匀性是决定学习表现的重要因素。尽管这些标准最初是为基于误差的学习方案而构思的，但追求哪一组模块对运动控制更有利的方法在一般的模块化其他研究中可能具有重要意义。
