The primary output of the nervous system is movement and behavior. While recent advances have democratized pose tracking during complex behavior, kinematic trajectories alone provide only indirect access to the underlying control processes. Here we present MIMIC-MJX, a framework for learning biologically-plausible neural control policies from kinematics. MIMIC-MJX models the generative process of motor control by training neural controllers that learn to actuate biomechanically-realistic body models in physics simulation to reproduce real kinematic trajectories. We demonstrate that our implementation is accurate, fast, data-efficient, and generalizable to diverse animal body models. Policies trained with MIMIC-MJX can be utilized to both analyze neural control strategies and simulate behavioral experiments, illustrating its potential as an integrative modeling framework for neuroscience.

翻译：神经系统的主要输出是运动与行为。尽管近期进展已使复杂行为过程中的姿态追踪技术趋于普及，但仅凭运动学轨迹只能间接反映底层的控制过程。本文提出MIMIC-MJX框架，该框架能够从运动学数据中学习具有生物学合理性的神经控制策略。MIMIC-MJX通过训练神经控制器来建模运动控制的生成过程，这些控制器学习在物理仿真中驱动生物力学真实的身体模型，以复现真实的运动学轨迹。我们证明该实现具有精确性、快速性、数据高效性，并能泛化至多种动物身体模型。通过MIMIC-MJX训练的策略既可用于分析神经控制策略，也可用于模拟行为实验，这展示了其作为神经科学集成建模框架的潜力。