Modeling motor control typically requires stitching together multiple neural and biomechanical modeling frameworks.
So, we created MotorNet — a toolbox to study neural architectures/learning, muscle dynamics, delays, noise, and tasks, all under one roof!
When we set out to study how neural networks interact with biomechanical models, we found that separate platforms are needed for neural and biomechanical modeling, and that existing biomechanical models are not differentiable — making training slow or unreliable
In the preprint, we lay out the structure of the toolbox, show a few examples of some classic motor control tasks, and replications of some of our favourite modeling work
To get started quickly — do a 'pip install motornet', check out the many tutorials included in the repo, or even open a tutorial directly in a colab notebook with a single click
https://oliviercodol.github.io/MotorNet/build/html/tutorials/train-net.html
New, complex tasks can be implemented, trained, and visualized quickly, speeding up the research cycle and providing tools that can be used by other researchers in the community
Finally, MotorNet provides a framework that can easily be expanded to more complex control scenarios. The only limit is your imagination!
Thanks to all our fantastic contributors and mentors who supported this work at every stage!
Olivier Codol is the first author (not on mastodon), and thanks to Mehrdad Kashefi, @andpru and @paulgribble
MotorNet is an open-source python toolbox built on Tensorflow that makes training neural networks to control realistic biomechanical models fast and accessible to non-experts, enabling teams to focus on concepts and ideas over implementation.
https://oliviercodol.github.io/MotorNet/build/html/index.html