In this work, we analyze a simplified, dynamical, closed-loop, neuromechanical simulation of insect joint control. We are specifically interested in two elements: (1) how slow muscle fibers may serve as temporal integrators of sensory feedback and (2) the role of common inhibitory (CI) motor neurons in resetting this integration when the commanded position changes, particularly during steady-state walking. Despite the simplicity of the model, we show that slow muscle fibers increase the accuracy of limb positioning, even for motions much shorter than the relaxation time of the fiber; this increase in accuracy is due to the slow dynamics of the fibers; the CI motor neuron plays a critical role in accelerating muscle relaxation when the limb moves to a new position; as in the animal, this architecture enables the control of the stance phase speed, independent of swing phase amplitude or duration, by changing the gain of sensory feedback to the stance phase muscles. We discuss how this relates to other models, and how it could be applied to robotic control.

中文翻译：

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探索常见抑制剂运动神经元在昆虫运动中的作用的神经力学模型。

在这项工作中，我们分析了昆虫关节控制的简化，动态，闭环，神经机械仿真。我们对两个元素特别感兴趣：（1）慢肌纤维如何充当感觉反馈的时间积分器；（2）当命令位置改变时，尤其是在稳定状态下，常见抑制（CI）运动神经元在重置该积分中的作用状态的步行。尽管模型简单，但我们显示，即使运动比纤维的松弛时间短得多，慢速肌肉纤维也可以提高肢体定位的准确性。精度的提高归因于纤维的缓慢动力学；当肢体移至新位置时，CI运动神经元在加速肌肉松弛中起关键作用；就像动物一样，这种架构可以控制姿态相位速度，通过改变对姿势相位肌肉的感觉反馈的增益，与摆动相位振幅或持续时间无关。我们讨论了这与其他模型之间的关系，以及如何将其应用于机器人控制。