Self-configuring feedback loops for sensorimotor control

  1. Sergio Oscar Verduzco-Flores  Is a corresponding author
  2. Erik De Schutter
  1. Okinawa Institute of Science and Technology, Japan

Abstract

How dynamic interactions between nervous system regions in mammals performs online motor control remains an unsolved problem. In this paper we show that feedback control is a simple, yet powerful way to understand the neural dynamics of sensorimotor control. We make our case using a minimal model comprising spinal cord, sensory and motor cortex, coupled by long connections that are plastic. It succeeds in learning how to perform reaching movements of a planar arm with 6 muscles in several directions from scratch. The model satisfies biological plausibility constraints, like neural implementation, transmission delays, local synaptic learning and continuous online learning. Using differential Hebbian plasticity the model can go from motor babbling to reaching arbitrary targets in less than 10 minutes of in silico time. Moreover, independently of the learning mechanism, properly configured feedback control has many emergent properties: neural populations in motor cortex show directional tuning and oscillatory dynamics, the spinal cord creates convergent force fields that add linearly, and movements are ataxic (as in a motor system without a cerebellum).

Data availability

The current manuscript is a computational study, so no data have been generated for this manuscript.The source code to generate all figures is available as two commented Jupyter notebooks. They can be downloaded from the following repository:https://gitlab.com/sergio.verduzco/public_materials/-/tree/master/adaptive_plasticityInstructions are in the "readme.md" file. Briefly:Prerequisites for running the notebooks are:- Python 3.5 or above (https://www.python.org)- Jupyter (https://jupyter.org)- Draculab (https://gitlab.com/sergio.verduzco/draculab)Please see the links above for detailed installation instructions.

Article and author information

Author details

  1. Sergio Oscar Verduzco-Flores

    Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Japan
    For correspondence
    sergio.verduzco@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0712-145X
  2. Erik De Schutter

    Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Onna-son, Japan
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8618-5138

Funding

No external funding was received for this work.

Reviewing Editor

  1. Juan Álvaro Gallego, Imperial College London, United Kingdom

Version history

  1. Received: January 20, 2022
  2. Accepted: October 26, 2022
  3. Accepted Manuscript published: November 14, 2022 (version 1)
  4. Version of Record published: November 24, 2022 (version 2)

Copyright

© 2022, Verduzco-Flores & De Schutter

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. Sergio Oscar Verduzco-Flores
  2. Erik De Schutter
(2022)
Self-configuring feedback loops for sensorimotor control
eLife 11:e77216.
https://doi.org/10.7554/eLife.77216

Share this article

https://doi.org/10.7554/eLife.77216

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