Flying insects use feedback from various sensory modalities including vision and mechanosensation to navigate through their environment. The rapid speed of mechanosensory information acquisition and processing compensates for the slower processing times associated with vision, particularly under low light conditions. While halteres in dipteran species are well known to provide such information for flight control, less is understood about the mechanosensory roles of their evolutionary antecedent, wings. The features that wing mechanosensory neurons (campaniform sensilla) encode remains relatively unexplored. We hypothesized that the wing campaniform sensilla of the hawkmoth, Manduca sexta, rapidly and selectively extract mechanical stimulus features in a manner similar to halteres. We used electrophysiological and computational techniques to characterize the encoding properties of wing campaniform sensilla. To accomplish this, we developed a novel technique for localizing receptive fields using a focused IR laser that elicits changes in the neural activity of mechanoreceptors. We found that (i) most wing mechanosensors encoded mechanical stimulus features rapidly and precisely, (ii) they are selective for specific stimulus features, and (iii) there is diversity in the encoding properties of wing campaniform sensilla. We found that the encoding properties of wing campaniform sensilla are similar to those for haltere neurons. Therefore, it appears that the neural architecture that underlies the haltere sensory function is present in wings, which lends credence to the notion that wings themselves may serve a similar sensory function. Thus, wings may not only function as the primary actuator of the organism but also as sensors of the inertial dynamics of the animal.
Calibration Object File
The “20150615 Calibration file_20pt.csv” file contains the physical measurements in millimeters of our 20-point calibration object (coordinate system based on an origin point) and is used to calibrate a volume of space in which the object of interest moves in.
Calibration Object Digitized Volume.zip
Spike Sorted Neural Data (time stamps of spiking)
Each file contains the spike timestamps of the units isolated from the extracellular data of a particular moth (M# refers to a particular moth). Each column represents a unit and each row corresponds to a timestamp of when a unit spiked. The last column of each file contains the unsorted spike timestamps, and therefore, was not used for further analyses. Spike sorting was performed using Offline Sorter V4 and timestamp data was exported from NeuroExplorer V5.
Spike Sorted Neural Data (Time stamps of spiking).zip
Spike Train Data Structures Resulting From White Noise Stimulus
The spike train data structure consists of a matrix in which each of the 30 columns represents the white noise stimulus segments and the 400,000 rows represent each of the sample indices (sample rate of 40kHz over 10 seconds results in 400,000 sample points). Furthermore, this matrix is binary in which the value “one” indicates the sample index that this unit spiked and zeros represent non-spiking indices. The two folders, “Base Spike Trains” and “NonBase Spike Trains”, contain the spike train data for the corresponding wing base and non-localized units. These data structures were created using Matlab code.
Spike Train Data Structures During White noise.zip
Wand Calibration Data
We verified the wing motion reconstruction data through cross validating that the measured length of a physical wand object can be reconstructed through 3D high-speed videography. The “20150615 Calibration file_20pt.csv” file contains the physical measurements of our calibration object and is used to calibrate the volume of space that the wand moved through. The “cal01_WandDLTcoefs.csv” file contains the calibrated volume coefficients. “M1_Wand.cine” and “M2_Wand.cine” are the recorded video files (using phantom software) that capture the wand movement through the calibrated space. “Digitized_Wandxyzpts.csv” contains the 3D coordinates of the wand position at each frame. The cameras were set at 1000 fps.The remaining files are output files not used in further analyses. Calibration and Digitization was performed with custom matlab codes by the Hedrick laboratory at UNC.
Wing Video and Reconstruction Data
The “UpSampled_Generalized Base Displacement.mat” and “Generalized Base Displacement.mat” files contain the generalized wing base displacement transformed from the wing tip displacement. The first file is upsampled so that the sampling rate of the data is 40 kHz, while the later file is sampled at 1 kHz. The “Tip Motor Stim.mat” and “Motor Tip Displacement.mat” files contain the wing tip displacements of one 10-second white noise segement (first file) and for 10 10-second white noise repeats (later file). Each tip displacement file is sample at 40 kHz. Each “Moth Wing Video” folder contains the 3D high-speed videography data of that moth wing. A total of four moth wings (2 males (files labeled with M16 or M27) and 2 females (files labeled with M26 or M28)) were used to transform their wing base and tip displacements into a generalizable wing base displacement. Each “Moth Wing Video” folder contains a “DLTcoefs.csv” file (calibration file), two “.cine” files (Video data of each camera sampled at 1000 fps), and a “xyzpts.csv” file that contains the reconstructed 3D coordinates of the digitized points at each frame. The remaining files are output files not used in further analyses. Calibration and Digitization was performed with custom matlab codes by the Hedrick laboratory at UNC. High-speed videography was conducted using phantom software.
Raw Extracellular Neural Data and Stimulus Data
Column one and two for the files labeled M1 through M25 are the raw neural voltage data and the corresponding motor voltage stimulus sampled at 40 kHz. M# indicates the particular moth used for experimentation. The raw neural data and stimulus data for M26 through M33 (both sampled at 40 kHz) were split into two files respectively called “M#_Raw Neural Data.txt” and “M#_ Stimulus Data.txt”. Files labeled M13, M15, M18, M19, and M20 had an additional step stimulus preceding the delivery of the white noise stimulus. A 0.2 Hz square wave stimulus of 4 V amplitude was interjected between the sinusoidal and white noise stimulus segments. The duration of the step stimulus was 16 seconds (640000 samples). There was a rest period of 1 second between the sinusoidal and the step function as well as between the step and white noise stimulus. Moreover, the Raw Neural Data files for M26, M28, M31, M32, and M33 contained multiple columns. Each column represented the neural data recorded from a particular recording site on the multi-site extracellular electrode. Having neural data from multiple recoding sites on the electrode improved spike sorting. These Data were acquired and saved in Matlab. Moreover, these data consisted of multiple chunks (6 chunks), which were concatenated together.