Lehr's fields of campaniform sensilla in beetles (Coleoptera): Functional morphology. I. General part and allometry

Frantsevich, Leonid; Gorb, Stanislav N.; Radchenko, Vladimir G.; Gladun, Dmytro; Polilov, Alexey A.

doi:10.1016/j.asd.2014.09.001

Cited by 10 publications

(12 citation statements)

References 18 publications

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“…Direct comparisons between sensor counts of other insects in the literature is difficult because current reports are incomplete, focusing on specific sensor types. Even though this is a very small species of dragonfly, the counts of equivalent sensor classes are significantly higher than those reported on the wings of moths, flies or beetles described so far 12,45,60 . One reason that might explain the great variation in wing sensory innervation across insects is the cost of maintaining the sensory neurons at the wing tips.…”

Section: A Complete Sensor Map Of Odonata Wingscontrasting

confidence: 57%

“…Extrapolating from this, we estimate this dragonfly has over 3000 wing sensors on its four wings, and the damselfly approximately half as many. Even though this is a very small species of dragonfly, these sensor counts are significantly higher than those reported on the wings of moths, flies or beetles described so far (Dickerson et al, 2014;Cole and Palka, 1982;Frantsevich et al, 2014). The dorsal side of the grasshopper wings were mapped previously, showing sensor counts comparable to those of the dragonfly (Albert et al, 1976).…”

Section: A Complete Sensor Map Of Odonata Wingsmentioning

confidence: 51%

“…Another key contributor for metabolic costs is the total neuron count, and the dragonfly wing contains a relatively large number of sensory neurons. Even though our full-wing mapping data is collected from one of the smallest dragonfly species, sensor counts are higher than equivalents reported on the wings of moths, flies or beetles described so far (Dickerson et al, 2014;Cole and Palka, 1982;Frantsevich et al, 2014). Direct comparisons between sensor counts of other insects in the literature are difficult because current reports tend to be incomplete, focusing on specific sensor types rather than a complete sensory ensemble.…”

Section: Metabolic Investment In the Dragonfly Wing Sensory Systemmentioning

confidence: 88%

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Complete neuroanatomy of dragonfly wings suggests direct sensing of aeroelastic deformations

Fabian

Siwanowicz

Uhrhan

et al. 2021

Preprint

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Fly-by-feel describes how flying animals capture aerodynamic information via their wings' sensory system to implement or enhance flight control. Traditional studies on animal flight emphasized controlling body stability via visual or inertial sensory inputs. In line with this, it has been demonstrated that wing sensory systems can provide inertial state estimation for the body. What about the state estimation of the wings themselves? Little is known about how flying animals utilize their wing sensory systems to monitor the dynamic state of their highly deformable wings. This study is a step toward a comprehensive investigation of how a flying animal senses aerodynamic and aeroelastic features of the wings relevant to flight control. Odonates: dragonflies and damselflies, are a great model for this because they have excellent flight performance and their wing structure has been extensively studied. Here, we developed a strategy to map the entire sensory system of Odonata wings via confocal microscopy. The result is the first complete map of a flying animal's wing sensory system, including both the external sensor morphologies and internal neuroanatomy. This complete search revealed over 750 sensors on each wing for one of the smallest dragonfly species and roughly half for a comparable size damselfly. We found over eight morphological classes of sensors, most of which resembled mechanosensors. Most sensors were innervated by a single neuron with an innervation pattern consistent with minimising wiring length. We further mapped the major veins of 13 Odonate species across 10 families and identified consistent sensor distribution patterns, with sensor count scaling with wing length. To explain the strain sensor density distribution along the major veins, we constructed a high-fidelity finite element model of a dragonfly wing based on micro-CT data. This flapping wing model revealed dynamic strain fields and suggested how increasing sensor count could allow encoding of different wing states. Taken together, the Odonate wing sensory system is well-equipped to implement sophisticated fly-by-feel flight control.

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Section: A Complete Sensor Map Of Odonata Wingscontrasting

confidence: 57%

Section: A Complete Sensor Map Of Odonata Wingsmentioning

confidence: 51%

Section: Metabolic Investment In the Dragonfly Wing Sensory Systemmentioning

confidence: 88%

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Complete neuroanatomy of dragonfly wings suggests direct sensing of aeroelastic deformations

Fabian

Siwanowicz

Uhrhan

et al. 2021

Preprint

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“…Dragonfly wing contains a relatively large number of sensory neurons with long axons. Even though our full-wing mapping data were collected from one of the smallest dragonfly species, sensor counts are higher than equivalents reported on the wings of moths, flies, or beetles described so far ( Dickerson et al., 2014 ) ( Cole and Palka, 1982 ) ( Frantsevich et al., 2014 ). Despite the large sensor counts, strain sensors on the dragonfly wings still form sparser distributions posteriorly and distally.…”

Section: Discussionmentioning

confidence: 72%

Systematic characterization of wing mechanosensors that monitor airflow and wing deformations

Fabian¹,

Siwanowicz²,

Uhrhan³

et al. 2022

iScience

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“…Despite these differences, some generalizations can be made about the placement of strain sensors (campaniform sensilla). Perhaps the most striking commonality across diverse taxa is the relative abundance of campaniform sensilla near the wing base [ 47 – 49 , 51 , 53 – 55 ]. Taken together, the results in the present work suggest that clustering sensors at the wing base may serve as a catch-all strategy, favorable across a wide range of wing and neural encoding properties to detect rotations about multiple axes (Figs 3 and 4 and Fig F in S1 Appendix ).…”

Section: Discussionmentioning

confidence: 99%

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

2021

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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.

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Lehr's fields of campaniform sensilla in beetles (Coleoptera): Functional morphology. I. General part and allometry

Cited by 10 publications

References 18 publications

Complete neuroanatomy of dragonfly wings suggests direct sensing of aeroelastic deformations

Complete neuroanatomy of dragonfly wings suggests direct sensing of aeroelastic deformations

Systematic characterization of wing mechanosensors that monitor airflow and wing deformations

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

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