Structural dynamics and neural representation of wing deformation

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 the 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 wing’s state under normal flapping conditions is detected by the spike timing of a few sensors, with additional sensors recruited under perturbation. The functional integration of wing biomechanics and sensor placement enables a straightforward solution for information transfer.
, , , # Structural dynamics and neural representation of wing deformation

Dataset DOI: [10.5061/dryad.41ns1rnqh](https://doi.org/10.5061/dryad.41ns1rnqh)

## Description of the data and file structure

***Free-flight kinematics***

Digitized high speed video recorded at 2000 fps.

***Takeoff kinematics***

Digitized high speed video recorded at 1000 fps.

***Electrophysiology***

Time series extracellular voltage of anterior nerve primary afferents and spike times for sorted units sampled at 60K Hz.

***Fixed wing deformations***

Displacement measurements of video frames recorded at 1000fps obtained using Ncorr software (see ncorr.com).

***Finite Element Analysis and Fluid-Structure Simulation***

3D wing mesh (.stl). See Fabian et al., 2022 for model construction details.

### Files and variables

#### File: FreeFlightKinematicsData.mat

**Description:** Free-flight Kinematics data

| Variable | Definition |
| :------------- | ...,