Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry

We can easily learn and perform a variety of movements that fundamentally require complex neuromuscular control. Many empirical findings have demonstrated that a wide range of complex muscle activation patterns could be well captured by the combination of a few functional modules, the so-called muscle synergies. Modularity represented by muscle synergies would simplify the control of a redundant neuromuscular system. However, how the reduction of neuromuscular redundancy through a modular controller contributes to sensorimotor learning remains unclear. To clarify such roles, we constructed a simple neural network model of the motor control system that included three intermediate layers representing neurons in the primary motor cortex, spinal interneurons organized into modules and motoneurons controlling upper-arm muscles. After a model learning period to generate the desired shoulder and/or elbow joint torques, we compared the adaptation to a novel rotational perturbation between modul...人类可轻松学习并完成各类动作，而此类动作本质上均依赖复杂的神经肌肉控制。诸多实证研究证实，大量复杂的肌肉激活模式可通过少量功能模块的组合得到精准表征，即所谓的肌肉协同（muscle synergies）。以肌肉协同为代表的模块化结构，可简化冗余神经肌肉系统的控制流程。然而，经由模块化控制器实现神经肌肉冗余度降低的机制，究竟如何促进感觉运动学习，目前仍无定论。为阐明上述作用机制，我们构建了一款简化的运动控制系统神经网络模型，该模型包含三层中间结构：分别对应初级运动皮层神经元、模块化组织的脊髓中间神经元，以及支配上臂肌肉的运动神经元。在模型完成一段学习以生成目标肩关节和/或肘关节力矩后，我们对比了模块化控制器与[原文未完整表述的对比对象]对新型旋转扰动的适应能力。