Biology subjects

Weber, A. I.

Publications and source records attributed to Weber, A. I..

6 recordsLinked to original sources

bioRxiv2026

Pilifers provide rapid mechanosensory feedback during floral tracking in hawkmoths

Hawkmoths expertly hover over flowers during feeding, accurately tracking floral movements even as flowers gently sway in the wind. They use visual and mechanosensory feedback to adjust their position, while their long, flexible mouthpart, the proboscis, stays inserted inside the nectar spur. Although much is known about how hawkmoths detect and process visual motion, far less is understood about how hawkmoths detect mechanical motion cues. Pilifers are hair bundles at the proboscis-head joint, well-positioned to provide feedback about floral motion. Here, we tested if pilifers could provide mechanosensory feedback essential for successful floral tracking. We computed bending at the proboscis base during floral tracking in freely flying hawkmoths and found that floral movements induce small yet significantly correlated lateral bending. Recordings from pilifer afferents showed that they respond to lateral motions of amplitudes similar to those seen in natural floral tracking behaviors. They respond to different directions and speeds of proboscis motion with an average latency of 1.9 ms. Neural tracing revealed extensive sensory arborizations in the sub-esophageal ganglion of the brain and ventral nerve cord, indicating a feedback pathway to the neck and flight motor system that enables rapid coordination of appendages and body for successful feeding.

neuroscience↗

bioRxiv2023

Intraspecific variation in the placement of campaniform sensilla on the wings of the hawkmoth Manduca sexta

Flight control requires active sensory feedback, and insects have many sensors that help them estimate their current locomotor state, including campaniform sensilla, which are mechanoreceptors that sense strain resulting from deformation of the cuticle. Campaniform sensilla on the wing detect bending and torsional forces encountered during flight, providing input to the flight feedback control system. During flight, wings experience complex spatio-temporal strain patterns. Because campaniform sensilla detect only local strain, their placement on the wing is presumably critical for determining the overall representation of wing deformation; however, how these sensilla are distributed across wings is largely unknown. Here, we test the hypothesis that campaniform sensilla are found in stereotyped locations across individuals of Manduca sexta, a hawkmoth. We found that although campaniform sensilla are consistently found on the same veins or in the same regions of the wings, their total number and distribution can vary extensively. This suggests that there is some robustness to variation in sensory feedback in the insect flight control system. The regions where campaniform sensilla are consistently found provide clues to their functional roles, although some patterns might be reflective of developmental processes. Collectively, our results on intraspecific variation in campaniform sensilla placement on insect wings will help reshape our thinking on the utility of mechanosensory feedback for insect flight control and guide further experimental and comparative studies.

neuroscience↗

bioRxiv2022

Nonuniform structural properties of wings confer sensing advantages

Sensory feedback is essential to both animals and robotic systems for achieving coordinated, precise movements. Mechanosensory feedback, which provides information about body deformation and position in space, depends not only on the properties of sensors but also on the structure in which they are embedded. In insects, wing structure plays a particularly important role in flapping flight: in addition to generating aerodynamic forces, wings provide mechanosensory feedback necessary for guiding flight while undergoing dramatic deformations over the course of each wingbeat. However, the role that wing structure plays in determining mechanosensory information is relatively unexplored. Insect wings exhibit characteristic stiffness gradients, with greatest stiffness at the base and leading edge and lowest stiffness at the tip of the trailing edge. Additionally, wings are subject to both aerodynamic and structural damping. The sensory consequences of stiffness gradients and damping are unknown. Here we examine how both the nonuniform stiffness profile of a wing and its damping impacts sensory performance, using finite elements analysis combined with sensor placement optimization approaches. We show that wings with nonuniform stiffness exhibit several advantages over uniform stiffness wings, resulting in higher accuracy of rotation detection and lower sensitivity to the placement of sensors on the wing. Moreover, we show that higher damping generally improves the accuracy with which body rotations can be detected. These results contribute to our understanding of the evolution of the nonuniform stiffness patterns in insect wings, as well as suggest design principles for robotic systems.

neuroscience↗

bioRxiv2022

Branch point evolution controls species-specific alternative splicing and regulates long term potentiation

Regulation and functionality of species-specific alternative splicing has remained enigmatic to the present date. Calcium/calmodulin-dependent protein kinase II{beta} (CaMKII{beta}) is expressed in several splice variants and plays a key role in learning and memory. Here, we identify and characterize several primate-specific CAMK2B splice isoforms, which show altered kinetic properties and changes in substrate specificity. Furthermore, we demonstrate that primate-specific Camk2{beta} alternative splicing is achieved through branch point weakening during evolution. We show that reducing branch point and splice site strengths during evolution globally renders constitutive exons alternative, thus providing a paradigm for cis-directed species-specific alternative splicing regulation. Using CRISPR/Cas9 we introduced a weaker human branch point into the mouse genome, resulting in human-like CAMK2B splicing in the brain of mutant mice. We observe a strong impairment of long-term potentiation in CA3-CA1 synapses of mutant mice, thus connecting branch point-controlled, species-specific alternative splicing with a fundamental function in learning and memory.

molecular biology↗

bioRxiv2021

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 enables stable flight control. While the impacts of wing structure on aerodynamic performance have been widely studied, the impacts of wing structure on sensing remain 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 over a flapping wing to detect body rotation about different axes, using a computational wing model with varying flexural stiffness inspired by the hawkmoth Manduca sexta. A small set of mechanosensors, conveying strain information at key locations with a single action potential per wingbeat, permit 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 their joint evolution.

neuroscience↗

bioRxiv2020

Identification of multiple noise sources improves estimation of neural responses across stimulus conditions

Most models of neural responses are constructed to capture the average response to inputs but poorly reproduce the observed response variability. The origins and structure of this variability have significant implications for how information is encoded and processed in the nervous system. Here, we present a new modeling framework that better captures observed features of neural response variability across stimulus conditions by incorporating multiple sources of noise. We use this model to fit responses of retinal ganglion cells at two different ambient light levels and demonstrate that it captures the full distribution of responses. The model reveals light level-dependent changes that could not be seen with previous models. It shows both large changes in rectification of nonlinear circuit elements and systematic differences in the contributions of different noise sources under different conditions. This modeling framework is general and can be applied to a variety of systems outside the retina.

neuroscience↗