Contrasting action and posture coding with hierarchical deep neural network models of proprioception

  1. Kai J Sandbrink
  2. Pranav Mamidanna
  3. Claudio Michaelis
  4. Matthias Bethge
  5. Mackenzie W Mathis  Is a corresponding author
  6. Alexander Mathis  Is a corresponding author
  1. École Polytechnique Fédérale de Lausanne, Switzerland
  2. Eberhard Karls Universität Tübingen, Germany

Abstract

Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical role of proprioception is representing the body state. We hypothesize that the proprioceptive system could also be critical for high-level tasks such as action recognition. To test this theory, we pursued a task-driven modeling approach, which allowed us to isolate the study of proprioception. We generated a large synthetic dataset of human arm trajectories tracing characters of the Latin alphabet in 3D space, together with muscle activities obtained from a musculoskeletal model and model-based muscle spindle activity. Next, we compared two classes of tasks: trajectory decoding and action recognition, which allowed us to train hierarchical models to decode either the position and velocity of the end-effector of one's posture or the character (action) identity from the spindle firing patterns. We found that artificial neural networks could robustly solve both tasks, and the networks'units show tuning properties similar to neurons in the primate somatosensory cortex and the brainstem. Remarkably, we found uniformly distributed directional selective units only with the action-recognition-trained models and not the trajectory-decoding-trained models. This suggests that proprioceptive encoding is additionally associated with higher-level functions such as action recognition and therefore provides new, experimentally testable hypotheses of how proprioception aids in adaptive motor control.

Data availability

The computational dataset and code to create it is available at https://github.com/amathislab/DeepDraw

Article and author information

Author details

  1. Kai J Sandbrink

    Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Genève, Switzerland
    Competing interests
    No competing interests declared.
  2. Pranav Mamidanna

    Tübingen AI Center, Eberhard Karls Universität Tübingen, Tübingen, Germany
    Competing interests
    No competing interests declared.
  3. Claudio Michaelis

    Tübingen AI Center, Eberhard Karls Universität Tübingen, Tübingen, Germany
    Competing interests
    No competing interests declared.
  4. Matthias Bethge

    Tübingen AI Center, Eberhard Karls Universität Tübingen, Tübingen, Germany
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6417-7812
  5. Mackenzie W Mathis

    Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Genève, Switzerland
    For correspondence
    mackenzie@post.harvard.edu
    Competing interests
    Mackenzie W Mathis, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7368-4456
  6. Alexander Mathis

    Brain Mind Institute, École Polytechnique Fédérale de Lausanne, Genève, Switzerland
    For correspondence
    alexander.mathis@epfl.ch
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3777-2202

Funding

Swiss National Science Foundation (310030_201057)

  • Mackenzie W Mathis

Swiss National Science Foundation (310030_212516)

  • Alexander Mathis

Rowland Institute at Harvard

  • Kai J Sandbrink
  • Mackenzie W Mathis
  • Alexander Mathis

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Demba Ba, Harvard University, United States

Version history

  1. Preprint posted: May 8, 2020 (view preprint)
  2. Received: June 30, 2022
  3. Accepted: May 16, 2023
  4. Accepted Manuscript published: May 31, 2023 (version 1)
  5. Version of Record published: July 21, 2023 (version 2)

Copyright

© 2023, Sandbrink et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Kai J Sandbrink
  2. Pranav Mamidanna
  3. Claudio Michaelis
  4. Matthias Bethge
  5. Mackenzie W Mathis
  6. Alexander Mathis
(2023)
Contrasting action and posture coding with hierarchical deep neural network models of proprioception
eLife 12:e81499.
https://doi.org/10.7554/eLife.81499

Share this article

https://doi.org/10.7554/eLife.81499

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