Abstract
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.
Abstract (translated)
在运动神经学中，人工循环神经网络模型常常可以补充动物研究。然而，大多数建模努力都局限于数据匹配，而仅少数研究在强化学习上下文中研究了虚拟具有身体感知能力的实体，这些实体不能直接与生物体进行直接比较。我们的研究解决这个问题，通过揭示执行腿动能力的虚拟机器人的结构化神经网络活动，直接支持多足动物步行和骑车的实验研究结果。我们发现，训练步行的实体表现出平滑的动态特性，避免纠缠或相邻神经网络空间中的反向神经网络轨迹，这是计算神经学的基本原理之一。具体来说，在所有的步伐类型中，实体的循环层神经网络轨迹都比输入驱动控制层的轨迹更容易纠缠。为了更好地解释这些椭圆形轨迹的神经网络分离，我们确定了速度轴，这些轴最大限度地提高了不同向前、向后、旋转速度条件下的均值活动多样性。
URL
https://arxiv.org/abs/2305.11107
