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MIMIC-MJX: Neuromechanical Emulation of Animal Behavior.
Charles Y Zhang1, Yuanjia Yang2,3, Aidan Sirbu4,5
1Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
Arxiv
|December 8, 2025
Summary
We developed MIMIC-MJX, a novel framework for creating neural control policies from movement data. This approach allows for accurate modeling of animal behavior and neural control strategies in neuroscience research.
Area of Science:
- Computational Neuroscience
- Biophysics
- Robotics
Background:
- Movement and behavior are key outputs of the nervous system.
- Kinematic data alone offers limited insight into neural control mechanisms.
- Existing pose-tracking methods lack direct access to underlying control processes.
Purpose of the Study:
- To present MIMIC-MJX, a framework for learning biologically-plausible neural control policies from kinematic data.
- To model the generative process of motor control using neural controllers and physics simulations.
- To enable analysis of neural control strategies and simulation of behavioral experiments.
Main Methods:
- Trained neural controllers within a physics simulation environment.
- Utilized biomechanically realistic body models.
- Reproduced real-world kinematic trajectories to learn control policies.
Main Results:
- MIMIC-MJX demonstrates accuracy, speed, and data efficiency.
- The framework is generalizable across diverse animal body models.
- Learned policies effectively capture neural control strategies.
Conclusions:
- MIMIC-MJX provides a powerful tool for understanding neural control of behavior.
- The framework facilitates the analysis of neural control strategies.
- It serves as an integrative modeling framework for neuroscience research.
