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Morphology and the gradient of a symmetric potential predict gait transitions of dogs
Gaits and gait transitions play a central role in the movement of animals. Symmetry is thought to govern the structure of the nervous system, and constrain the limb motions of quadrupeds. We quantify the symmetry of dog gaits with respect to combinations of bilateral, fore–aft, and spatio-temporal s...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5506184/ https://www.ncbi.nlm.nih.gov/pubmed/28631166 http://dx.doi.org/10.1007/s00422-017-0721-2 |
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author | Wilshin, Simon Haynes, G. Clark Porteous, Jack Koditschek, Daniel Revzen, Shai Spence, Andrew J. |
author_facet | Wilshin, Simon Haynes, G. Clark Porteous, Jack Koditschek, Daniel Revzen, Shai Spence, Andrew J. |
author_sort | Wilshin, Simon |
collection | PubMed |
description | Gaits and gait transitions play a central role in the movement of animals. Symmetry is thought to govern the structure of the nervous system, and constrain the limb motions of quadrupeds. We quantify the symmetry of dog gaits with respect to combinations of bilateral, fore–aft, and spatio-temporal symmetry groups. We tested the ability of symmetries to model motion capture data of dogs walking, trotting and transitioning between those gaits. Fully symmetric models performed comparably to asymmetric with only a [Formula: see text] increase in the residual sum of squares and only one-quarter of the parameters. This required adding a spatio-temporal shift representing a lag between fore and hind limbs. Without this shift, the symmetric model residual sum of squares was [Formula: see text] larger. This shift is related to (linear regression, [Formula: see text] , [Formula: see text] ) dog morphology. That this symmetry is respected throughout the gaits and transitions indicates that it generalizes outside a single gait. We propose that relative phasing of limb motions can be described by an interaction potential with a symmetric structure. This approach can be extended to the study of interaction of neurodynamic and kinematic variables, providing a system-level model that couples neuronal central pattern generator networks and mechanical models. |
format | Online Article Text |
id | pubmed-5506184 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-55061842017-07-27 Morphology and the gradient of a symmetric potential predict gait transitions of dogs Wilshin, Simon Haynes, G. Clark Porteous, Jack Koditschek, Daniel Revzen, Shai Spence, Andrew J. Biol Cybern Original Article Gaits and gait transitions play a central role in the movement of animals. Symmetry is thought to govern the structure of the nervous system, and constrain the limb motions of quadrupeds. We quantify the symmetry of dog gaits with respect to combinations of bilateral, fore–aft, and spatio-temporal symmetry groups. We tested the ability of symmetries to model motion capture data of dogs walking, trotting and transitioning between those gaits. Fully symmetric models performed comparably to asymmetric with only a [Formula: see text] increase in the residual sum of squares and only one-quarter of the parameters. This required adding a spatio-temporal shift representing a lag between fore and hind limbs. Without this shift, the symmetric model residual sum of squares was [Formula: see text] larger. This shift is related to (linear regression, [Formula: see text] , [Formula: see text] ) dog morphology. That this symmetry is respected throughout the gaits and transitions indicates that it generalizes outside a single gait. We propose that relative phasing of limb motions can be described by an interaction potential with a symmetric structure. This approach can be extended to the study of interaction of neurodynamic and kinematic variables, providing a system-level model that couples neuronal central pattern generator networks and mechanical models. Springer Berlin Heidelberg 2017-06-19 2017 /pmc/articles/PMC5506184/ /pubmed/28631166 http://dx.doi.org/10.1007/s00422-017-0721-2 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Wilshin, Simon Haynes, G. Clark Porteous, Jack Koditschek, Daniel Revzen, Shai Spence, Andrew J. Morphology and the gradient of a symmetric potential predict gait transitions of dogs |
title | Morphology and the gradient of a symmetric potential predict gait transitions of dogs |
title_full | Morphology and the gradient of a symmetric potential predict gait transitions of dogs |
title_fullStr | Morphology and the gradient of a symmetric potential predict gait transitions of dogs |
title_full_unstemmed | Morphology and the gradient of a symmetric potential predict gait transitions of dogs |
title_short | Morphology and the gradient of a symmetric potential predict gait transitions of dogs |
title_sort | morphology and the gradient of a symmetric potential predict gait transitions of dogs |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5506184/ https://www.ncbi.nlm.nih.gov/pubmed/28631166 http://dx.doi.org/10.1007/s00422-017-0721-2 |
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