发表机构
Peking University; State Key Laboratory of Transvascular Implantation Devices; Peking-Tsinghua Center for Life Sciences; The Second Affiliated Hospital of Zhejiang University School of Medicine（北京大学; 经血管植入器械全国重点实验室; 北大-清华生命科学联合中心; 浙江大学医学院附属第二医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过机器人模拟揭示运动皮层旋转神经动力学与精确运动间的因果联系，发现神经旋转优化轨迹调整，并启发类动物智能机器人。
AI 中文摘要
动物证据表明，精确的随意运动源于运动皮层中的旋转神经群体动力学，但其物理效应仍属未知。我们开发了一种生物运动系统的机器人模拟，配备人工肌肉、多模态传感器以及通过强化学习训练的神经网络控制器。该机器人模拟表现出精确的运动、对损伤的鲁棒性以及与动物相似的神经群体动力学。这种任务驱动、具身化的模型阐明了神经群体动力学与运动结果之间的因果联系。我们发现，神经旋转产生与到达方向正交的振荡机动，优化轨迹调整，这一点已通过灵长类神经数据得到证实。该模型还揭示了在传感器和运动冗余下的反直觉神经能量原理，以及运动学习过程中引人注目的尤里卡时刻，从而弥合了生物系统与人工系统之间的鸿沟。这些发现为神经动力学如何促成精确而灵活的运动提供了新视角，并启发了未来具有类动物机动性的智能机器人。
英文摘要
Animal evidence shows that precise voluntary movements arise from rotational neural population dynamics in motor cortex, but their physical effects remain unknown. We developed a robotic analog of biological motor systems with artificial muscles, multimodal sensors, and a neural network controller trained via reinforcement learning. The robotic analog exhibited accurate movements, robustness to damage, and neural population dynamics akin to animals. This task-driven, embodied model illuminates the causal link between neural population dynamics and motor outcomes. We discovered that neural rotations generate oscillatory maneuvers orthogonal to the reaching direction, optimizing trajectory adjustments, which is confirmed by primate neural data. The model also revealed counterintuitive neural energy principles under sensor and motor redundancies, and striking Eureka moments during motor learning, bridging biological and artificial systems. These findings provide new perspectives on how neural dynamics contribute to accurate and flexible movement, inspiring future intelligent robots with animal-like mobility.
