Gait Generation and Its Energy Efficiency Based on Rat Neuromusculoskeletal Model.
central pattern generator
energy efficiency
muscle synergy
neuromusculoskeletal model
rat
trot
walk
Journal
Frontiers in neuroscience
ISSN: 1662-4548
Titre abrégé: Front Neurosci
Pays: Switzerland
ID NLM: 101478481
Informations de publication
Date de publication:
2019
2019
Historique:
received:
29
06
2019
accepted:
27
11
2019
entrez:
4
2
2020
pubmed:
6
2
2020
medline:
6
2
2020
Statut:
epublish
Résumé
Changing gait is crucial for adaptive and smooth animal locomotion. Although it remains unclear what makes animals decide on a specific gait, energy efficiency is an important factor. It has been reported that the relationship of oxygen consumption with speed is U-shaped for each horse gait and that different gaits have different speeds at which oxygen consumption is minimized. This allows the horse to produce energy-efficient locomotion in a wide speed range by changing gait. However, the underlying mechanisms causing oxygen consumption to be U-shaped and the speeds for the minimum consumption to be different between different gaits are unclear. In the present study, we used a neuromusculoskeletal model of the rat to examine the mechanism from a dynamic viewpoint. Specifically, we constructed the musculoskeletal part of the model based on empirical anatomical data on rats and the motor control model based on the physiological concepts of the spinal central pattern generator and muscle synergy. We also incorporated the posture and speed regulation models at the levels of the brainstem and cerebellum. Our model achieved walking through forward dynamic simulation, and the simulated joint kinematics and muscle activities were compared with animal data. Our model also achieved trotting by changing only the phase difference of the muscle-synergy-based motor commands between the forelimb and hindlimb. Furthermore, the speed of each gait varied by changing only the extension phase duration and amplitude of the muscle synergy-based motor commands and the reference values for the regulation models. The relationship between cost of transport (CoT) and speed was U-shaped for both the generated walking and trotting, and the speeds for the minimum CoT were different for the two gaits, as observed in the oxygen consumption of horses. We found that the resonance property and the posture and speed regulations contributed to the CoT shape and difference in speeds for the minimum CoT. We further discussed the energy efficiency of gait based on the simulation results.
Identifiants
pubmed: 32009870
doi: 10.3389/fnins.2019.01337
pmc: PMC6978804
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1337Informations de copyright
Copyright © 2020 Toeda, Aoi, Fujiki, Funato, Tsuchiya and Yanagihara.
Références
J Physiol. 2019 Jun;597(12):3147-3165
pubmed: 30916787
Proc Natl Acad Sci U S A. 2017 Aug 8;114(32):8643-8648
pubmed: 28739958
Physiol Behav. 1999 Jul;66(5):723-9
pubmed: 10405098
Acta Neurobiol Exp (Wars). 1999;59(2):131-44
pubmed: 10497818
PLoS One. 2015 Jul 28;10(7):e0133936
pubmed: 26218076
J Neurophysiol. 2005 Dec;94(6):4256-68
pubmed: 16049149
J Exp Psychol Hum Percept Perform. 1995 Feb;21(1):183-202
pubmed: 7707029
J Neurophysiol. 2001 Jul;86(1):447-62
pubmed: 11431524
J Theor Biol. 1990 Feb 9;142(3):359-91
pubmed: 2338828
J Exp Biol. 2006 Oct;209(Pt 19):3925-39
pubmed: 16985208
J Mov Disord. 2017 Jan;10(1):1-17
pubmed: 28122432
Neuroscientist. 2006 Aug;12(4):339-48
pubmed: 16840710
Biol Cybern. 2004 Feb;90(2):146-55
pubmed: 14999481
J Exp Biol. 1988 Sep;138:301-18
pubmed: 3193059
J Neurophysiol. 2012 Apr;107(8):2057-71
pubmed: 22190626
Nat Neurosci. 2002 Nov;5(11):1226-35
pubmed: 12404008
J Exp Biol. 2012 Jan 15;215(Pt 2):287-300
pubmed: 22189773
J Neurophysiol. 2005 Aug;94(2):1057-65
pubmed: 15800075
J Neurophysiol. 2005 Jan;93(1):609-13
pubmed: 15342720
J Neurophysiol. 2006 Jun;95(6):3426-37
pubmed: 16554517
Science. 2011 Nov 18;334(6058):997-9
pubmed: 22096202
J Theor Biol. 2006 Feb 7;238(3):636-45
pubmed: 16084529
J Neurophysiol. 2013 Oct;110(7):1511-24
pubmed: 23615542
J Neural Eng. 2015 Oct;12(5):056014
pubmed: 26331532
J Appl Physiol. 1963 Mar;18:367-70
pubmed: 13932993
Biol Cybern. 2013 Apr;107(2):201-16
pubmed: 23430278
Vet J. 2013 Dec;198 Suppl 1:e20-6
pubmed: 24511634
Neurosci Res. 2016 Mar;104:88-95
pubmed: 26616311
Nature. 2012 Aug 30;488(7413):642-6
pubmed: 22932389
Equine Vet J Suppl. 2010 Nov;(38):455-61
pubmed: 21059045
Science. 1991 Jul 19;253(5017):306-8
pubmed: 1857965
Sci Rep. 2019 Jan 23;9(1):369
pubmed: 30674970
J Physiol. 2008 Mar 1;586(5):1239-45
pubmed: 18202098
Nat Neurosci. 2003 Mar;6(3):300-8
pubmed: 12563264
Sci Rep. 2015 Feb 02;5:8169
pubmed: 25639661
Am J Phys Anthropol. 2011 Aug;145(4):568-80
pubmed: 21590751
J Physiol. 2006 Dec 1;577(Pt 2):617-39
pubmed: 17008376
Bioinspir Biomim. 2015 Sep 09;10(5):055004
pubmed: 26351756
J Morphol. 1973 Sep;141(1):1-41
pubmed: 4727469
Nat Neurosci. 1999 Feb;2(2):162-7
pubmed: 10195201
J Neurophysiol. 2018 Nov 1;120(5):2484-2497
pubmed: 30133381
Sci Rep. 2018 Nov 26;8(1):17341
pubmed: 30478405
J R Soc Interface. 2013 Feb 06;10(81):20120908
pubmed: 23389894
J Exp Biol. 2014 Jul 15;217(Pt 14):2434-6
pubmed: 25031454