Cargando…

A model for self-organization of sensorimotor function: spinal interneuronal integration

Control of musculoskeletal systems depends on integration of voluntary commands and somatosensory feedback in the complex neural circuits of the spinal cord. It has been suggested that the various connectivity patterns that have been identified experimentally may result from the many transcriptional...

Descripción completa

Detalles Bibliográficos
Autores principales: Enander, Jonas M. D., Loeb, Gerald E., Jörntell, Henrik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Physiological Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9293245/
https://www.ncbi.nlm.nih.gov/pubmed/35475709
http://dx.doi.org/10.1152/jn.00054.2022
_version_ 1784749574678642688
author Enander, Jonas M. D.
Loeb, Gerald E.
Jörntell, Henrik
author_facet Enander, Jonas M. D.
Loeb, Gerald E.
Jörntell, Henrik
author_sort Enander, Jonas M. D.
collection PubMed
description Control of musculoskeletal systems depends on integration of voluntary commands and somatosensory feedback in the complex neural circuits of the spinal cord. It has been suggested that the various connectivity patterns that have been identified experimentally may result from the many transcriptional types that have been observed in spinal interneurons. We ask instead whether the muscle-specific details of observed connectivity patterns can arise as a consequence of Hebbian adaptation during early development, rather than being genetically ordained. We constructed an anatomically simplified model musculoskeletal system with realistic muscles and sensors and connected it to a recurrent, random neuronal network consisting of both excitatory and inhibitory neurons endowed with Hebbian learning rules. We then generated a wide set of randomized muscle twitches typical of those described during fetal development and allowed the network to learn. Multiple simulations consistently resulted in diverse and stable patterns of activity and connectivity that included subsets of the interneurons that were similar to “archetypical” interneurons described in the literature. We also found that such learning led to an increased degree of cooperativity between interneurons when performing larger limb movements on which it had not been trained. Hebbian learning gives rise to rich sets of diverse interneurons whose connectivity reflects the mechanical properties of the system. At least some of the transcriptomic diversity may reflect the effects of this process rather than the cause of the connectivity. Such a learning process seems better suited to respond to the musculoskeletal mutations that underlie the evolution of new species. NEW & NOTEWORTHY We present a model of a self-organizing early spinal cord circuitry, which is attached to a biologically realistic sensorized musculoskeletal system. Without any a priori-defined connectivity or organization, learning induced by spontaneous, fetal-like motor activity results in the emergence of a well-functioning spinal interneuronal circuit whose connectivity patterns resemble in many respects those observed in the adult mammalian spinal cord. Hence, our result questions the importance of genetically controlled wiring for spinal cord function.
format Online
Article
Text
id pubmed-9293245
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher American Physiological Society
record_format MEDLINE/PubMed
spelling pubmed-92932452022-08-01 A model for self-organization of sensorimotor function: spinal interneuronal integration Enander, Jonas M. D. Loeb, Gerald E. Jörntell, Henrik J Neurophysiol Research Article Control of musculoskeletal systems depends on integration of voluntary commands and somatosensory feedback in the complex neural circuits of the spinal cord. It has been suggested that the various connectivity patterns that have been identified experimentally may result from the many transcriptional types that have been observed in spinal interneurons. We ask instead whether the muscle-specific details of observed connectivity patterns can arise as a consequence of Hebbian adaptation during early development, rather than being genetically ordained. We constructed an anatomically simplified model musculoskeletal system with realistic muscles and sensors and connected it to a recurrent, random neuronal network consisting of both excitatory and inhibitory neurons endowed with Hebbian learning rules. We then generated a wide set of randomized muscle twitches typical of those described during fetal development and allowed the network to learn. Multiple simulations consistently resulted in diverse and stable patterns of activity and connectivity that included subsets of the interneurons that were similar to “archetypical” interneurons described in the literature. We also found that such learning led to an increased degree of cooperativity between interneurons when performing larger limb movements on which it had not been trained. Hebbian learning gives rise to rich sets of diverse interneurons whose connectivity reflects the mechanical properties of the system. At least some of the transcriptomic diversity may reflect the effects of this process rather than the cause of the connectivity. Such a learning process seems better suited to respond to the musculoskeletal mutations that underlie the evolution of new species. NEW & NOTEWORTHY We present a model of a self-organizing early spinal cord circuitry, which is attached to a biologically realistic sensorized musculoskeletal system. Without any a priori-defined connectivity or organization, learning induced by spontaneous, fetal-like motor activity results in the emergence of a well-functioning spinal interneuronal circuit whose connectivity patterns resemble in many respects those observed in the adult mammalian spinal cord. Hence, our result questions the importance of genetically controlled wiring for spinal cord function. American Physiological Society 2022-06-01 2022-04-27 /pmc/articles/PMC9293245/ /pubmed/35475709 http://dx.doi.org/10.1152/jn.00054.2022 Text en Copyright © 2022 The Authors https://creativecommons.org/licenses/by/4.0/Licensed under Creative Commons Attribution CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/) . Published by the American Physiological Society.
spellingShingle Research Article
Enander, Jonas M. D.
Loeb, Gerald E.
Jörntell, Henrik
A model for self-organization of sensorimotor function: spinal interneuronal integration
title A model for self-organization of sensorimotor function: spinal interneuronal integration
title_full A model for self-organization of sensorimotor function: spinal interneuronal integration
title_fullStr A model for self-organization of sensorimotor function: spinal interneuronal integration
title_full_unstemmed A model for self-organization of sensorimotor function: spinal interneuronal integration
title_short A model for self-organization of sensorimotor function: spinal interneuronal integration
title_sort model for self-organization of sensorimotor function: spinal interneuronal integration
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9293245/
https://www.ncbi.nlm.nih.gov/pubmed/35475709
http://dx.doi.org/10.1152/jn.00054.2022
work_keys_str_mv AT enanderjonasmd amodelforselforganizationofsensorimotorfunctionspinalinterneuronalintegration
AT loebgeralde amodelforselforganizationofsensorimotorfunctionspinalinterneuronalintegration
AT jorntellhenrik amodelforselforganizationofsensorimotorfunctionspinalinterneuronalintegration
AT enanderjonasmd modelforselforganizationofsensorimotorfunctionspinalinterneuronalintegration
AT loebgeralde modelforselforganizationofsensorimotorfunctionspinalinterneuronalintegration
AT jorntellhenrik modelforselforganizationofsensorimotorfunctionspinalinterneuronalintegration