Computational reproductions of external force field adaption without assuming desired trajectories
Computational reproductions of external force field adaption without assuming desired trajectories
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DOI：
10.1016/j.neunet.2021.01.030
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
H. Kambara;Atsushi Takagi;Haruka Shimizu;Toshihiro Kawase;N. Yoshimura;N. Schweighofer;Y. Koike
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文献类型：
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作者：
H. Kambara;Atsushi Takagi;Haruka Shimizu;Toshihiro Kawase;N. Yoshimura;N. Schweighofer;Y. Koike
最佳反馈控制是一个用于表征人类运动的既定框架。然而，目前尚不完全了解大脑如何通过与环境的相互作用来计算最佳增益。在过去的研究中，我们提出了一种运动学习模型，该模型识别一组反馈和前馈控制器以及手臂肌肉骨骼系统的状态预测器来控制自由到达运动。在这项研究中，我们将该模型应用于强制场适应任务，其中正常的伸手运动会受到施加在手上的外力的干扰。在没有关于手臂和环境的先验知识的情况下，该模型能够通过产生反作用力来适应力场，以类似于行为文献中报道的方式克服它。我们的模型生成的运动的运动学与力场适应前后观察到的人类运动具有共同的特征。此外，我们证明了模型中引入的结构和学习算法引起了端点平衡位置的变化和静态力调制，并伴随着快速和慢速的学习过程。重要的是，我们的模型不需要所需的轨迹，无需指定运动持续时间即可产生运动，并通过探索环境来预测力的生成模式。我们的模型展示了一种可能的机制，通过该机制，中枢神经系统可以通过不断更新身体的肌肉骨骼模型来控制和适应点对点的到达运动，而无需指定所需的轨迹。
Optimal feedback control is an established framework that is used to characterize human movement. However, it is not fully understood how the brain computes optimal gains through interactions with the environment. In the past study, we proposed a model of motor learning that identifies a set of feedback and feedforward controllers and a state predictor of the arm musculoskeletal system to control free reaching movements. In this study, we applied the model to force field adaptation tasks where normal reaching movements are disturbed by an external force imposed on the hand. Without a priori knowledge about the arm and environment, the model was able to adapt to the force field by generating counteracting forces to overcome it in a manner similar to what is reported in the behavioral literature. The kinematics of the movements generated by our model share characteristic features of human movements observed before and after force field adaptation. In addition, we demonstrate that the structure and learning algorithm introduced in our model induced a shift in the end-point’s equilibrium position and a static force modulation, accompanied by a fast and a slow learning process. Importantly, our model does not require desired trajectories, yields movements without specifying movement duration, and predicts force generation patterns by exploring the environment. Our model demonstrates a possible mechanism through which the central nervous system may control and adapt a point-to-point reaching movement without specifying a desired trajectory by continuously updating the body’s musculoskeletal model.