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

Publication 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.

Metrics

  • 411
    Page views
  • 69
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Andrew McKinney, Ming Hu ... Xiaolong Jiang
    Research Article

    The locus coeruleus (LC) houses the vast majority of noradrenergic neurons in the brain and regulates many fundamental functions including fight and flight response, attention control, and sleep/wake cycles. While efferent projections of the LC have been extensively investigated, little is known about its local circuit organization. Here, we performed large-scale multi-patch recordings of noradrenergic neurons in adult mouse LC to profile their morpho-electric properties while simultaneously examining their interactions. LC noradrenergic neurons are diverse and could be classified into two major morpho-electric types. While fast excitatory synaptic transmission among LC noradrenergic neurons was not observed in our preparation, these mature LC neurons connected via gap junction at a rate similar to their early developmental stage and comparable to other brain regions. Most electrical connections form between dendrites and are restricted to narrowly spaced pairs or small clusters of neurons of the same type. In addition, more than two electrically coupled cell pairs were often identified across a cohort of neurons from individual multi-cell recording sets that followed a chain-like organizational pattern. The assembly of LC noradrenergic neurons thus follows a spatial and cell type-specific wiring principle that may be imposed by a unique chain-like rule.

    1. Computational and Systems Biology
    Damiano Sgarbossa, Umberto Lupo, Anne-Florence Bitbol
    Research Article

    Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generating novel sequences belonging to protein families. Protein language models trained on multiple sequence alignments, such as MSA Transformer, are highly attractive candidates to this end. We propose and test an iterative method that directly employs the masked language modeling objective to generate sequences using MSA Transformer. We demonstrate that the resulting sequences score as well as natural sequences, for homology, coevolution and structure-based measures. For large protein families, our synthetic sequences have similar or better properties compared to sequences generated by Potts models, including experimentally-validated ones. Moreover, for small protein families, our generation method based on MSA Transformer outperforms Potts models. Our method also more accurately reproduces the higher-order statistics and the distribution of sequences in sequence space of natural data than Potts models. MSA Transformer is thus a strong candidate for protein sequence generation and protein design.