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Do muscle synergies reduce the dimensionality of behavior?

机译:肌肉协同作用会降低行为的维度吗?

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摘要

The muscle synergy hypothesis is an archetype of the notion of Dimensionality Reduction (DR) occurring in the central nervous system due to modular organization. Toward validating this hypothesis, it is important to understand if muscle synergies can reduce the state-space dimensionality while maintaining task control. In this paper we present a scheme for investigating this reduction utilizing the temporal muscle synergy formulation. Our approach is based on the observation that constraining the control input to a weighted combination of temporal muscle synergies also constrains the dynamic behavior of a system in a trajectory-specific manner. We compute this constrained reformulation of system dynamics and then use the method of system balancing for quantifying the DR; we term this approach as Trajectory Specific Dimensionality Analysis (TSDA). We then investigate the consequence of minimization of the dimensionality for a given task. These methods are tested in simulations on a linear (tethered mass) and a non-linear (compliant kinematic chain) system. Dimensionality of various reaching trajectories is compared when using idealized temporal synergies. We show that as a consequence of this Minimum Dimensional Control (MDC) model, smooth straight-line Cartesian trajectories with bell-shaped velocity profiles emerged as the optima for the reaching task. We also investigated the effect on dimensionality due to adding via-points to a trajectory. The results indicate that a trajectory and synergy basis specific DR of behavior results from muscle synergy control. The implications of these results for the synergy hypothesis, optimal motor control, motor development, and robotics are discussed.
机译:肌肉协同增效假说是由于模块化组织而在中枢神经系统中发生的降维(DR)概念的原型。为了验证该假设,重要的是要了解肌肉协同作用是否可以在维持任务控制的同时降低状态空间维数。在本文中,我们提出了一种利用颞肌协同制剂研究这种减少的方案。我们的方法基于以下观察结果:将控制输入约束为时间肌肉协同作用的加权组合,也以轨迹特定的方式约束系统的动态行为。我们计算出这种约束的系统动力学公式,然后使用系统平衡的方法来量化DR。我们将此方法称为“轨迹特定维分析(TSDA)”。然后,我们调查给定任务的维数最小化的结果。这些方法在线性(系留质量)和非线性(顺应运动链)系统的仿真中进行了测试。当使用理想的时间协同时,比较各种到达轨迹的维数。我们显示,由于此最小尺寸控制(MDC)模型的出现,具有钟形速度轮廓的平滑直线笛卡尔轨迹出现了作为到达任务的最佳选择。我们还研究了由于在轨迹上添加过孔点而对尺寸产生的影响。结果表明,基于肌肉协同控制,行为的轨迹和协同基础特定DR。讨论了这些结果对协同假设,最佳电机控制,电机开发和机器人技术的影响。

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