Aoi Shinya, Ogihara Naomichi, Funato Tetsuro, Sugimoto Yasuhiro, Tsuchiya Kazuo
Department of Aeronautics and Astronautics, Graduate School of Engineering, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan.
Biol Cybern. 2010 May;102(5):373-87. doi: 10.1007/s00422-010-0373-y. Epub 2010 Mar 9.
The central pattern generators (CPGs) in the spinal cord strongly contribute to locomotor behavior. To achieve adaptive locomotion, locomotor rhythm generated by the CPGs is suggested to be functionally modulated by phase resetting based on sensory afferent or perturbations. Although phase resetting has been investigated during fictive locomotion in cats, its functional roles in actual locomotion have not been clarified. Recently, simulation studies have been conducted to examine the roles of phase resetting during human bipedal walking, assuming that locomotion is generated based on prescribed kinematics and feedback control. However, such kinematically based modeling cannot be used to fully elucidate the mechanisms of adaptation. In this article we proposed a more physiologically based mathematical model of the neural system for locomotion and investigated the functional roles of phase resetting. We constructed a locomotor CPG model based on a two-layered hierarchical network model of the rhythm generator (RG) and pattern formation (PF) networks. The RG model produces rhythm information using phase oscillators and regulates it by phase resetting based on foot-contact information. The PF model creates feedforward command signals based on rhythm information, which consists of the combination of five rectangular pulses based on previous analyses of muscle synergy. Simulation results showed that our model establishes adaptive walking against perturbing forces and variations in the environment, with phase resetting playing important roles in increasing the robustness of responses, suggesting that this mechanism of regulation may contribute to the generation of adaptive human bipedal locomotion.
脊髓中的中枢模式发生器（CPGs）对运动行为有重要贡献。为了实现适应性运动，CPGs产生的运动节律被认为是通过基于感觉传入或扰动的相位重置进行功能调节的。尽管在猫的虚拟运动过程中已经对相位重置进行了研究，但其在实际运动中的功能作用尚未明确。最近，已经进行了模拟研究来检验人类双足行走过程中相位重置的作用，假设运动是基于规定的运动学和反馈控制产生的。然而，这种基于运动学的建模不能完全阐明适应机制。在本文中，我们提出了一个更基于生理学的运动神经系统数学模型，并研究了相位重置的功能作用。我们基于节律发生器（RG）和模式形成（PF）网络的两层层次网络模型构建了一个运动CPG模型。RG模型使用相位振荡器产生节律信息，并根据足部接触信息通过相位重置对其进行调节。PF模型根据节律信息创建前馈命令信号，该信号由基于先前肌肉协同分析的五个矩形脉冲组合而成。模拟结果表明，我们的模型能够建立针对干扰力和环境变化的适应性行走，相位重置在提高反应的稳健性方面发挥着重要作用，这表明这种调节机制可能有助于产生适应性人类双足运动。
