In motor neuroscience, artificial recurrent neural networks models often complement animal studies. However, most modeling efforts are limited to data-fitting, and the few that examine virtual embodied agents in a reinforcement learning context, do not draw direct comparisons to their biological counterparts. Our study addressing this gap, by uncovering structured neural activity of a virtual robot performing legged locomotion that directly support experimental findings of primate walking and cycling. We find that embodied agents trained to walk exhibit smooth dynamics that avoid tangling -- or opposing neural trajectories in neighboring neural space -- a core principle in computational neuroscience. Specifically, across a wide suite of gaits, the agent displays neural trajectories in the recurrent layers are less tangled than those in the input-driven actuation layers. To better interpret the neural separation of these elliptical-shaped trajectories, we identify speed axes that maximizes variance of mean activity across different forward, lateral, and rotational speed conditions.

翻译：在运动神经科学中，人工循环神经网络模型通常补充动物研究。然而，大多数建模工作局限于数据拟合，少数涉及强化学习背景下虚拟具身智能体的研究，并未与其生物对应物进行直接比较。本研究弥补了这一空白，揭示了执行腿部运动的虚拟机器人的结构化神经活动，直接支持了灵长类动物行走与骑行的实验发现。我们发现，训练用于行走的具身智能体展现出避免缠绕——即相邻神经空间中相反的神经轨迹——的平滑动力学，这是计算神经科学中的一个核心原理。具体而言，在一系列广泛步态中，智能体在循环层中的神经轨迹比在输入驱动的致动层中缠绕更少。为了更好地解释这些椭圆形状轨迹的神经分离，我们识别了速度轴，该轴最大化不同前向、侧向和旋转速度条件下平均活动的方差。