Wing structure and neural encoding jointly determine sensing strategies in insect flight.

Animals rely on sensory feedback to generate accurate, reliable movements. In many flying insects, strain-sensitive neurons on the wings provide rapid feedback that is critical for stable flight control. While the impacts of wing structure on aerodynamic performance have been widely studied, the imp...

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Autores principales:	Alison I Weber, Thomas L Daniel, Bingni W Brunton
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/eac7378738f0447db67a68538f407c2b
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id	oai:doaj.org-article:eac7378738f0447db67a68538f407c2b
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spelling	oai:doaj.org-article:eac7378738f0447db67a68538f407c2b2021-12-02T19:58:06ZWing structure and neural encoding jointly determine sensing strategies in insect flight.1553-734X1553-735810.1371/journal.pcbi.1009195https://doaj.org/article/eac7378738f0447db67a68538f407c2b2021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009195https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Animals rely on sensory feedback to generate accurate, reliable movements. In many flying insects, strain-sensitive neurons on the wings provide rapid feedback that is critical for stable flight control. While the impacts of wing structure on aerodynamic performance have been widely studied, the impacts of wing structure on sensing are largely unexplored. In this paper, we show how the structural properties of the wing and encoding by mechanosensory neurons interact to jointly determine optimal sensing strategies and performance. Specifically, we examine how neural sensors can be placed effectively on a flapping wing to detect body rotation about different axes, using a computational wing model with varying flexural stiffness. A small set of mechanosensors, conveying strain information at key locations with a single action potential per wingbeat, enable accurate detection of body rotation. Optimal sensor locations are concentrated at either the wing base or the wing tip, and they transition sharply as a function of both wing stiffness and neural threshold. Moreover, the sensing strategy and performance is robust to both external disturbances and sensor loss. Typically, only five sensors are needed to achieve near-peak accuracy, with a single sensor often providing accuracy well above chance. Our results show that small-amplitude, dynamic signals can be extracted efficiently with spatially and temporally sparse sensors in the context of flight. The demonstrated interaction of wing structure and neural encoding properties points to the importance of understanding each in the context of their joint evolution.Alison I WeberThomas L DanielBingni W BruntonPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009195 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Alison I Weber Thomas L Daniel Bingni W Brunton Wing structure and neural encoding jointly determine sensing strategies in insect flight.
description	Animals rely on sensory feedback to generate accurate, reliable movements. In many flying insects, strain-sensitive neurons on the wings provide rapid feedback that is critical for stable flight control. While the impacts of wing structure on aerodynamic performance have been widely studied, the impacts of wing structure on sensing are largely unexplored. In this paper, we show how the structural properties of the wing and encoding by mechanosensory neurons interact to jointly determine optimal sensing strategies and performance. Specifically, we examine how neural sensors can be placed effectively on a flapping wing to detect body rotation about different axes, using a computational wing model with varying flexural stiffness. A small set of mechanosensors, conveying strain information at key locations with a single action potential per wingbeat, enable accurate detection of body rotation. Optimal sensor locations are concentrated at either the wing base or the wing tip, and they transition sharply as a function of both wing stiffness and neural threshold. Moreover, the sensing strategy and performance is robust to both external disturbances and sensor loss. Typically, only five sensors are needed to achieve near-peak accuracy, with a single sensor often providing accuracy well above chance. Our results show that small-amplitude, dynamic signals can be extracted efficiently with spatially and temporally sparse sensors in the context of flight. The demonstrated interaction of wing structure and neural encoding properties points to the importance of understanding each in the context of their joint evolution.
format	article
author	Alison I Weber Thomas L Daniel Bingni W Brunton
author_facet	Alison I Weber Thomas L Daniel Bingni W Brunton
author_sort	Alison I Weber
title	Wing structure and neural encoding jointly determine sensing strategies in insect flight.
title_short	Wing structure and neural encoding jointly determine sensing strategies in insect flight.
title_full	Wing structure and neural encoding jointly determine sensing strategies in insect flight.
title_fullStr	Wing structure and neural encoding jointly determine sensing strategies in insect flight.
title_full_unstemmed	Wing structure and neural encoding jointly determine sensing strategies in insect flight.
title_sort	wing structure and neural encoding jointly determine sensing strategies in insect flight.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/eac7378738f0447db67a68538f407c2b
work_keys_str_mv	AT alisoniweber wingstructureandneuralencodingjointlydeterminesensingstrategiesininsectflight AT thomasldaniel wingstructureandneuralencodingjointlydeterminesensingstrategiesininsectflight AT bingniwbrunton wingstructureandneuralencodingjointlydeterminesensingstrategiesininsectflight
_version_	1718375813532876800

Wing structure and neural encoding jointly determine sensing strategies in insect flight.

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