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Neural network models for spinal implementation of muscle synergies

View ORCID ProfileYunqing Song, View ORCID ProfileMasaya Hirashima, View ORCID ProfileTomohiko Takei
doi: https://doi.org/10.1101/2021.10.27.466061
Yunqing Song
1Graduate School of Medicine, Kyoto University
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Masaya Hirashima
2Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT)
3Graduate School of Frontier Biosciences, Osaka University
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Tomohiko Takei
1Graduate School of Medicine, Kyoto University
4The Hakubi Center for Advanced Research, Kyoto University
5Brain Science Institute, Tamagawa University
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  • For correspondence: takei@lab.tamagawa.ac.jp
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Abstract

Muscle synergies have been proposed as functional modules to simplify the complexity of body motor control; however, their neural implementation is still unclear. Converging evidence suggests that output projections of the spinal premotor interneurons (PreM-INs) underlie the formation of muscle synergies, but they exhibit a substantial variation across neurons and exclude standard models assuming a small number of unitary “modules” in the spinal cord. Here we compared neural network models for muscle synergies to seek a biologically plausible model that reconciles previous clinical and electrophysiological findings. We examined three neural network models: one with random connections (non-synergy model), one with a small number of spinal synergies (simple synergy model), and one with a large number of spinal neurons representing muscle synergies with a certain variation (population synergy model). We found that the simple and population synergy models emulate the robustness of muscle synergies against cortical stroke observed in human stroke patients. Furthermore, the size of the spinal variation of the population synergy matched well with the variation in spinal PreM-INs recorded in monkeys. These results suggest that a spinal population with moderate variation is a biologically plausible model for the neural implementation of muscle synergies.

Competing Interest Statement

The authors have declared no competing interest.

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-NC-ND 4.0 International license.
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Posted October 28, 2021.
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Neural network models for spinal implementation of muscle synergies
Yunqing Song, Masaya Hirashima, Tomohiko Takei
bioRxiv 2021.10.27.466061; doi: https://doi.org/10.1101/2021.10.27.466061
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Neural network models for spinal implementation of muscle synergies
Yunqing Song, Masaya Hirashima, Tomohiko Takei
bioRxiv 2021.10.27.466061; doi: https://doi.org/10.1101/2021.10.27.466061

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