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arXiv 1306.2065physics.bio-ph
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在阻力主导的介质中神经机械推进相的出现

Emergence of the advancing neuromechanical phase in a resistive force dominated medium

Yang Ding, Sarah S. Sharpe, Kurt Wiesenfeld, Daniel I. Goldman

摘要:

波动运动是一种步态,其中推力产生在身体弯曲的行波的相反方向上,是动物在流体中、陆地上甚至在沙子中使用的常见推进模式。因此,它一直是一个很好的系统,发现运动的神经力学原则。在几乎所有研究的动物中,肌肉激活波比身体弯曲波进展得更快,导致相对于朝向尾部的弯曲的激活推进阶段。这被称为“神经机械相位滞后”(NPL)。几个多参数神经力学模型再现了这一现象,但由于模型的复杂性,NPL的起源已被证明难以识别。在这里,我们使用也许是最简单的波动游泳模型来准确地预测NPL在沙鱼蜥蜴,没有拟合参数在沙游泳。在沙鱼运动中使用的正弦波,摩擦主导和非惯性颗粒阻力环境,和简单的模型允许详细的分析,并揭示了负责的现象的基本机制:从身体上的远点和本地旅行扭矩的同步扭矩的组合。这种一般机制应有助于解释在其他环境中的生物体的NPL,因此,我们建议,沙游泳可能是一个很好的系统,定量生成和测试其他神经力学模型的运动。这样的系统也可以为复杂环境中机器人波动运动的设计和控制提供指导。

英文摘要:

Undulatory locomotion, a gait in which thrust is produced in the opposite direction of a traveling wave of body bending, is a common mode of propulsion used by animals in fluids, on land, and even within sand. As such it has been an excellent system for discovery of neuromechanical principles of movement. In nearly all animals studied, the wave of muscle activation progresses faster than the wave of body bending, leading to an advancing phase of activation relative to the curvature towards the tail. This is referred to as "neuromechanical phase lags" (NPL). Several multi-parameter neuromechanical models have reproduced this phenomenon, but due to model complexity the origin of the NPL has proved difficult to identify. Here we use perhaps the simplest model of undulatory swimming to accurately predict the NPL during sand-swimming by the sandfish lizard, with no fitting parameters. The sinusoidal wave used in sandfish locomotion, the friction-dominated and non-inertial granular resistive force environment, and the simplicity of the model allow detailed analysis, and reveal the fundamental mechanism responsible for the phenomenon: the combination of synchronized torques from distant points on the body and local traveling torques. This general mechanism should help explain the NPL in organisms in other environments; we therefore propose that sand-swimming could be an excellent system to quantitatively generate and test other neuromechanical models of movement. Such a system can also provide guidance for the design and control of robotic undulatory locomotors in complex environments.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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