Yarger, A. M. | Search bioRxiv

bioRxiv2026

State dependant modulation of optic flow-processing lobula plate cells in butterflies

Increasing experimental evidence suggests that biological systems cancel predictable components of sensory signals while maintaining sensitivity to externally induced state changes. This strategy provides task-specific sensor responses for posture, locomotion, and gaze control. A prime example is found in interneurons that respond to visual image shifts resulting from the relative motion between an animal's eyes and its visual surroundings. Such optic flow-processing interneurons, found across phyla and are particularly well characterized in Dipteran and other flying insects. We studied optic flow-processing interneurons in the Monarch butterfly whose large and highly contrasted wings sweep through the visual field with every wing-beat cycle, potentially obscuring interneuron output signals. Our results show baseline spiking activity increases when animals flap their wings, and individual spikes are phase-locked to the wing-beat cycle, even in the dark, when no visual motion input is available. A qualitative estimate of the interneurons' response to directional wing motion through its receptive field is not sufficient to explain the recorded activity patterns. Our results suggest that an additional internal signal suppresses responses to wing-induced visual motion to support effective vision-based stabilization reflexes. These findings support the principle that self-generated signals are suppressed while sensitivity to external modulation is preserved.

neuroscience↗

bioRxiv2024

Mechanosensory representation of wing deformations

Locomotor control is facilitated by mechanosensory inputs that report how the body interacts with a physical medium. Effective representation of compliant wing deformations is particularly challenging due to the many degrees of freedom. Structural configurations can constrain the stimulus space, and strategic placement of sensors can simplify computation. Here, we measured and modelled wing displacement fields and characterized spatiotemporal encoding of the wing mechanosensors. Our data show how dragonfly wing architecture prescribes deformation modes consistent across models and measurements. We found that the wings state under normal flapping conditions is detected by the spike timing of few sensors, with additional sensors recruited under perturbation. The functional integration of wing biomechanics and sensor placement enables a straightforward solution for information transfer. Significance StatementMany systems in nature precisely control highly deformable structures, yet monitoring structural deformations has posed a significant challenge for biologists and engineers. By measuring and modelling the intricate structure of compliant dragonfly wings, we demonstrate an elegant solution for mechanosensory representation. We show that the functional integration of natural wing biomechanics and sensor placement provides a straightforward solution for information transfer. Wing morphology passively constrains the range of natural deformations, producing strain patterns that align with sensor locations, allowing them to monitor the wing using a simple timing-dependent encoding strategy. The way insects monitor aerodynamic and inertial forces via wing deformations during flight could apply to many sensory systems in nature and inspire artificial neural networks for controlling diverse dynamic systems.

neuroscience↗