Reaching movements, as a basic yet complex motor behavior, are a foundational model system in neuroscience. In particular, there has been a significant recent expansion of investigation into the neural circuit mechanisms of reach behavior in mice. Nevertheless, quantification of mouse reach kinematics remains lacking, limiting comparison to the primate literature. In this study, we quantitatively demonstrate the homology of mouse reach kinematics to primate reach, and also discover novel late-phase correlational structure that implies online control. Overall, our results highlight the decelerative phase of reach as important in driving successful outcome. Specifically, we develop and implement a novel statistical machine learning algorithm to identify kinematic features associated with successful reaches and find that late-phase kinematics are most predictive of outcome, signifying online reach control as opposed to pre-planning. Moreover, we identify and characterize late-phase kinematic adjustments that are yoked to mid-flight position and velocity of the limb, allowing for dynamic correction of initial variability, with head-fixed reaches being less dependent on position in comparison to freely-behaving reaches. Furthermore, consecutive reaches exhibit positional error-correction but not hot-handedness, implying opponent regulation of motor variability. Overall, our results establish foundational mouse reach kinematics in the context of neuroscientific investigation, characterizing mouse reach production as an active process that relies on dynamic online control mechanisms.

中文翻译：

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在线控制小鼠的到达精度

伸手动作作为一种基本但复杂的运动行为，是神经科学的基础模型系统。特别是，最近对小鼠伸手行为的神经回路机制的研究有了显着的扩展。然而，小鼠触及运动学的量化仍然缺乏，限制了与灵长类文献的比较。在这项研究中，我们定量地证明了小鼠触及运动学与灵长类动物触及的同源性，并且还发现了意味着在线控制的新型后期相关结构。总体而言，我们的结果强调了到达的减速阶段对于推动成功结果非常重要。具体来说，我们开发并实施了一种新颖的统计机器学习算法，以识别与成功到达相关的运动学特征，并发现后期运动学最能预测结果，这意味着在线到达控制而不是预先规划。此外，我们识别并描述了与肢体的飞行中位置和速度相关的后期运动学调整，允许对初始变异性进行动态校正，与自由行为的伸展相比，头部固定的伸展对位置的依赖性较小。此外，连续的伸展表现出位置误差校正，但不是急躁，这意味着对手对运动变化的调节。总的来说，我们的结果在神经科学研究的背景下建立了基础的小鼠触及运动学，