Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks

View ORCID ProfileOlivier Codol, View ORCID ProfileJonathan A. Michaels, Mehrdad Kashefi, View ORCID ProfileJ. Andrew Pruszyski, View ORCID ProfilePaul L. Gribble
doi: https://doi.org/10.1101/2023.02.17.528969
Olivier Codol
1Western Institute for Neuroscience, University of Western Ontario, Ontario, Canada
2Department of Psychology, University of Western Ontario, Ontario, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Olivier Codol
  • For correspondence: codol.olivier@gmail.com
Jonathan A. Michaels
1Western Institute for Neuroscience, University of Western Ontario, Ontario, Canada
3Department of Physiology & Pharmacology, Schulich School of Medicine & Dentistry, University of Western Ontario, Ontario, Canada
4Robarts Research Institute, University of Western Ontario, Ontario, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jonathan A. Michaels
Mehrdad Kashefi
1Western Institute for Neuroscience, University of Western Ontario, Ontario, Canada
3Department of Physiology & Pharmacology, Schulich School of Medicine & Dentistry, University of Western Ontario, Ontario, Canada
4Robarts Research Institute, University of Western Ontario, Ontario, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
J. Andrew Pruszyski
1Western Institute for Neuroscience, University of Western Ontario, Ontario, Canada
2Department of Psychology, University of Western Ontario, Ontario, Canada
3Department of Physiology & Pharmacology, Schulich School of Medicine & Dentistry, University of Western Ontario, Ontario, Canada
4Robarts Research Institute, University of Western Ontario, Ontario, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for J. Andrew Pruszyski
Paul L. Gribble
1Western Institute for Neuroscience, University of Western Ontario, Ontario, Canada
2Department of Psychology, University of Western Ontario, Ontario, Canada
3Department of Physiology & Pharmacology, Schulich School of Medicine & Dentistry, University of Western Ontario, Ontario, Canada
5Haskins Laboratories, New Haven CT, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Paul L. Gribble
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Artificial neural networks (ANNs) are a powerful class of computational models for unravelling neural mechanisms of brain function. However, for neural control of movement, they currently must be integrated with software simulating biomechanical effectors, leading to limiting impracticalities: (1) researchers must rely on two different platforms and (2) biomechanical effectors are not generally differentiable, constraining researchers to reinforcement learning algorithms despite the existence and potential biological relevance of faster training methods. To address these limitations, we developed MotorNet, an open-source Python toolbox for creating arbitrarily complex, differentiable, and biomechanically realistic effectors that can be trained on user-defined motor tasks using ANNs. MotorNet is designed to meet several goals: ease of installation, ease of use, a high-level user-friendly API, and a modular architecture to allow for flexibility in model building. MotorNet requires no dependencies outside Python, making it easy to get started with. For instance, it allows training ANNs on typically used motor control models such as a two joint, six muscle, planar arm within minutes on a typical desktop computer. MotorNet is built on TensorFlow and therefore can implement any network architecture that is possible using the TensorFlow framework. Consequently, it will immediately benefit from advances in artificial intelligence through TensorFlow updates. Finally, it is open source, enabling users to create and share their own improvements, such as new effector and network architectures or custom task designs. MotorNet’s focus on higher order model and task design will alleviate overhead cost to initiate computational projects for new researchers by providing a standalone, ready-to-go framework, and speed up efforts of established computational teams by enabling a focus on concepts and ideas over implementation.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • fixed minor typos updated funding sources

  • https://oliviercodol.github.io/MotorNet/build/html/index.html

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted February 19, 2023.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks
Olivier Codol, Jonathan A. Michaels, Mehrdad Kashefi, J. Andrew Pruszyski, Paul L. Gribble
bioRxiv 2023.02.17.528969; doi: https://doi.org/10.1101/2023.02.17.528969
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
MotorNet: a Python toolbox for controlling differentiable biomechanical effectors with artificial neural networks
Olivier Codol, Jonathan A. Michaels, Mehrdad Kashefi, J. Andrew Pruszyski, Paul L. Gribble
bioRxiv 2023.02.17.528969; doi: https://doi.org/10.1101/2023.02.17.528969

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4237)
  • Biochemistry (9155)
  • Bioengineering (6797)
  • Bioinformatics (24052)
  • Biophysics (12149)
  • Cancer Biology (9562)
  • Cell Biology (13814)
  • Clinical Trials (138)
  • Developmental Biology (7653)
  • Ecology (11729)
  • Epidemiology (2066)
  • Evolutionary Biology (15534)
  • Genetics (10663)
  • Genomics (14346)
  • Immunology (9502)
  • Microbiology (22876)
  • Molecular Biology (9113)
  • Neuroscience (49080)
  • Paleontology (357)
  • Pathology (1487)
  • Pharmacology and Toxicology (2576)
  • Physiology (3851)
  • Plant Biology (8347)
  • Scientific Communication and Education (1473)
  • Synthetic Biology (2299)
  • Systems Biology (6202)
  • Zoology (1302)