The impact of spontaneous movements on neuronal activity has created the need to quantify behavior. We present a versatile framework to directly capture the 3D motion of freely definable body points in a marker-free manner with high precision and reliability. Combining the tracking with neural recordings revealed multiplexing of information in the motor cortex neurons of freely moving rats. By integrating multiple behavioral variables into a model of the neural response, we derived a virtual head fixation for which the influence of specific body movements was removed. This strategy enabled us to analyze the behavior of interest (e.g., front paw movements). Thus, we unveiled an unexpectedly large fraction of neurons in the motor cortex with tuning to the paw movements, which was previously masked by body posture tuning. Once established, our framework can be efficiently applied to large datasets while minimizing the experimental workload caused by animal training and manual labeling.

自发运动对神经元活动的影响产生了量化行为的需要。我们提出了一个多功能框架，以高精度和可靠性以无标记方式直接捕获可自由定义的身体点的 3D 运动。将跟踪与神经记录相结合，揭示了自由移动大鼠运动皮层神经元中信息的多路复用。通过将多个行为变量整合到神经反应模型中，我们得出了一个虚拟头部固定，其中特定身体运动的影响被消除了。这种策略使我们能够分析感兴趣的行为（例如，前爪运动）。因此，我们通过调整爪子运动揭示了运动皮层中出乎意料的大部分神经元，这在以前被身体姿势调整所掩盖。一旦成立，