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

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 bo...

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Autores principales: Sandbrink, Kai J, Mamidanna, Pranav, Michaelis, Claudio, Bethge, Matthias, Mathis, Mackenzie Weygandt, Mathis, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361732/
https://www.ncbi.nlm.nih.gov/pubmed/37254843
http://dx.doi.org/10.7554/eLife.81499
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author Sandbrink, Kai J
Mamidanna, Pranav
Michaelis, Claudio
Bethge, Matthias
Mathis, Mackenzie Weygandt
Mathis, Alexander
author_facet Sandbrink, Kai J
Mamidanna, Pranav
Michaelis, Claudio
Bethge, Matthias
Mathis, Mackenzie Weygandt
Mathis, Alexander
author_sort Sandbrink, Kai J
collection PubMed
description 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.
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spelling pubmed-103617322023-07-22 Contrasting action and posture coding with hierarchical deep neural network models of proprioception Sandbrink, Kai J Mamidanna, Pranav Michaelis, Claudio Bethge, Matthias Mathis, Mackenzie Weygandt Mathis, Alexander eLife Computational and Systems Biology 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. eLife Sciences Publications, Ltd 2023-05-31 /pmc/articles/PMC10361732/ /pubmed/37254843 http://dx.doi.org/10.7554/eLife.81499 Text en © 2023, Sandbrink, Mamidanna et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Computational and Systems Biology
Sandbrink, Kai J
Mamidanna, Pranav
Michaelis, Claudio
Bethge, Matthias
Mathis, Mackenzie Weygandt
Mathis, Alexander
Contrasting action and posture coding with hierarchical deep neural network models of proprioception
title Contrasting action and posture coding with hierarchical deep neural network models of proprioception
title_full Contrasting action and posture coding with hierarchical deep neural network models of proprioception
title_fullStr Contrasting action and posture coding with hierarchical deep neural network models of proprioception
title_full_unstemmed Contrasting action and posture coding with hierarchical deep neural network models of proprioception
title_short Contrasting action and posture coding with hierarchical deep neural network models of proprioception
title_sort contrasting action and posture coding with hierarchical deep neural network models of proprioception
topic Computational and Systems Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10361732/
https://www.ncbi.nlm.nih.gov/pubmed/37254843
http://dx.doi.org/10.7554/eLife.81499
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