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Sensor-Motor Maps for Describing Linear Reflex Composition in Hopping

In human and animal motor control several sensory organs contribute to a network of sensory pathways modulating the motion depending on the task and the phase of execution to generate daily motor tasks such as locomotion. To better understand the individual and joint contribution of reflex pathways...

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Autores principales: Schumacher, Christian, Seyfarth, André
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5707192/
https://www.ncbi.nlm.nih.gov/pubmed/29230172
http://dx.doi.org/10.3389/fncom.2017.00108
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author Schumacher, Christian
Seyfarth, André
author_facet Schumacher, Christian
Seyfarth, André
author_sort Schumacher, Christian
collection PubMed
description In human and animal motor control several sensory organs contribute to a network of sensory pathways modulating the motion depending on the task and the phase of execution to generate daily motor tasks such as locomotion. To better understand the individual and joint contribution of reflex pathways in locomotor tasks, we developed a neuromuscular model that describes hopping movements. In this model, we consider the influence of proprioceptive length (LFB), velocity (VFB) and force feedback (FFB) pathways of a leg extensor muscle on hopping stability, performance and efficiency (metabolic effort). Therefore, we explore the space describing the blending of the monosynaptic reflex pathway gains. We call this reflex parameter space a sensor-motor map. The sensor-motor maps are used to visualize the functional contribution of sensory pathways in multisensory integration. We further evaluate the robustness of these sensor-motor maps to changes in tendon elasticity, body mass, segment length and ground compliance. The model predicted that different reflex pathway compositions selectively optimize specific hopping characteristics (e.g., performance and efficiency). Both FFB and LFB were pathways that enable hopping. FFB resulted in the largest hopping heights, LFB enhanced hopping efficiency and VFB had the ability to disable hopping. For the tested case, the topology of the sensor-motor maps as well as the location of functionally optimal compositions were invariant to changes in system designs (tendon elasticity, body mass, segment length) or environmental parameters (ground compliance). Our results indicate that different feedback pathway compositions may serve different functional roles. The topology of the sensor-motor map was predicted to be robust against changes in the mechanical system design indicating that the reflex system can use different morphological designs, which does not apply for most robotic systems (for which the control often follows a specific design). Consequently, variations in body mechanics are permitted with consistent compositions of sensory feedback pathways. Given the variability in human body morphology, such variations are highly relevant for human motor control.
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spelling pubmed-57071922017-12-11 Sensor-Motor Maps for Describing Linear Reflex Composition in Hopping Schumacher, Christian Seyfarth, André Front Comput Neurosci Neuroscience In human and animal motor control several sensory organs contribute to a network of sensory pathways modulating the motion depending on the task and the phase of execution to generate daily motor tasks such as locomotion. To better understand the individual and joint contribution of reflex pathways in locomotor tasks, we developed a neuromuscular model that describes hopping movements. In this model, we consider the influence of proprioceptive length (LFB), velocity (VFB) and force feedback (FFB) pathways of a leg extensor muscle on hopping stability, performance and efficiency (metabolic effort). Therefore, we explore the space describing the blending of the monosynaptic reflex pathway gains. We call this reflex parameter space a sensor-motor map. The sensor-motor maps are used to visualize the functional contribution of sensory pathways in multisensory integration. We further evaluate the robustness of these sensor-motor maps to changes in tendon elasticity, body mass, segment length and ground compliance. The model predicted that different reflex pathway compositions selectively optimize specific hopping characteristics (e.g., performance and efficiency). Both FFB and LFB were pathways that enable hopping. FFB resulted in the largest hopping heights, LFB enhanced hopping efficiency and VFB had the ability to disable hopping. For the tested case, the topology of the sensor-motor maps as well as the location of functionally optimal compositions were invariant to changes in system designs (tendon elasticity, body mass, segment length) or environmental parameters (ground compliance). Our results indicate that different feedback pathway compositions may serve different functional roles. The topology of the sensor-motor map was predicted to be robust against changes in the mechanical system design indicating that the reflex system can use different morphological designs, which does not apply for most robotic systems (for which the control often follows a specific design). Consequently, variations in body mechanics are permitted with consistent compositions of sensory feedback pathways. Given the variability in human body morphology, such variations are highly relevant for human motor control. Frontiers Media S.A. 2017-11-27 /pmc/articles/PMC5707192/ /pubmed/29230172 http://dx.doi.org/10.3389/fncom.2017.00108 Text en Copyright © 2017 Schumacher and Seyfarth. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Schumacher, Christian
Seyfarth, André
Sensor-Motor Maps for Describing Linear Reflex Composition in Hopping
title Sensor-Motor Maps for Describing Linear Reflex Composition in Hopping
title_full Sensor-Motor Maps for Describing Linear Reflex Composition in Hopping
title_fullStr Sensor-Motor Maps for Describing Linear Reflex Composition in Hopping
title_full_unstemmed Sensor-Motor Maps for Describing Linear Reflex Composition in Hopping
title_short Sensor-Motor Maps for Describing Linear Reflex Composition in Hopping
title_sort sensor-motor maps for describing linear reflex composition in hopping
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5707192/
https://www.ncbi.nlm.nih.gov/pubmed/29230172
http://dx.doi.org/10.3389/fncom.2017.00108
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