An optimality principle for locomotor central pattern generators

Abstract Two types of neural circuits contribute to legged locomotion: central pattern generators (CPGs) that produce rhythmic motor commands (even in the absence of feedback, termed “fictive locomotion”), and reflex circuits driven by sensory feedback. Each circuit alone serves a clear purpose, and...

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Autores principales:	Hansol X. Ryu, Arthur D. Kuo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/24b444faf5484b3386d5f2551449109d
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spelling	oai:doaj.org-article:24b444faf5484b3386d5f2551449109d2021-12-02T17:45:20ZAn optimality principle for locomotor central pattern generators10.1038/s41598-021-91714-12045-2322https://doaj.org/article/24b444faf5484b3386d5f2551449109d2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91714-1https://doaj.org/toc/2045-2322Abstract Two types of neural circuits contribute to legged locomotion: central pattern generators (CPGs) that produce rhythmic motor commands (even in the absence of feedback, termed “fictive locomotion”), and reflex circuits driven by sensory feedback. Each circuit alone serves a clear purpose, and the two together are understood to cooperate during normal locomotion. The difficulty is in explaining their relative balance objectively within a control model, as there are infinite combinations that could produce the same nominal motor pattern. Here we propose that optimization in the presence of uncertainty can explain how the circuits should best be combined for locomotion. The key is to re-interpret the CPG in the context of state estimator-based control: an internal model of the limbs that predicts their state, using sensory feedback to optimally balance competing effects of environmental and sensory uncertainties. We demonstrate use of optimally predicted state to drive a simple model of bipedal, dynamic walking, which thus yields minimal energetic cost of transport and best stability. The internal model may be implemented with neural circuitry compatible with classic CPG models, except with neural parameters determined by optimal estimation principles. Fictive locomotion also emerges, but as a side effect of estimator dynamics rather than an explicit internal rhythm. Uncertainty could be key to shaping CPG behavior and governing optimal use of feedback.Hansol X. RyuArthur D. KuoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hansol X. Ryu Arthur D. Kuo An optimality principle for locomotor central pattern generators
description	Abstract Two types of neural circuits contribute to legged locomotion: central pattern generators (CPGs) that produce rhythmic motor commands (even in the absence of feedback, termed “fictive locomotion”), and reflex circuits driven by sensory feedback. Each circuit alone serves a clear purpose, and the two together are understood to cooperate during normal locomotion. The difficulty is in explaining their relative balance objectively within a control model, as there are infinite combinations that could produce the same nominal motor pattern. Here we propose that optimization in the presence of uncertainty can explain how the circuits should best be combined for locomotion. The key is to re-interpret the CPG in the context of state estimator-based control: an internal model of the limbs that predicts their state, using sensory feedback to optimally balance competing effects of environmental and sensory uncertainties. We demonstrate use of optimally predicted state to drive a simple model of bipedal, dynamic walking, which thus yields minimal energetic cost of transport and best stability. The internal model may be implemented with neural circuitry compatible with classic CPG models, except with neural parameters determined by optimal estimation principles. Fictive locomotion also emerges, but as a side effect of estimator dynamics rather than an explicit internal rhythm. Uncertainty could be key to shaping CPG behavior and governing optimal use of feedback.
format	article
author	Hansol X. Ryu Arthur D. Kuo
author_facet	Hansol X. Ryu Arthur D. Kuo
author_sort	Hansol X. Ryu
title	An optimality principle for locomotor central pattern generators
title_short	An optimality principle for locomotor central pattern generators
title_full	An optimality principle for locomotor central pattern generators
title_fullStr	An optimality principle for locomotor central pattern generators
title_full_unstemmed	An optimality principle for locomotor central pattern generators
title_sort	optimality principle for locomotor central pattern generators
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/24b444faf5484b3386d5f2551449109d
work_keys_str_mv	AT hansolxryu anoptimalityprincipleforlocomotorcentralpatterngenerators AT arthurdkuo anoptimalityprincipleforlocomotorcentralpatterngenerators AT hansolxryu optimalityprincipleforlocomotorcentralpatterngenerators AT arthurdkuo optimalityprincipleforlocomotorcentralpatterngenerators
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An optimality principle for locomotor central pattern generators

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