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Raw data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, part 3

Citation

Turner, Maxwell (2022), Raw data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, part 3, Dryad, Dataset, https://doi.org/10.5061/dryad.j3tx95xjk

Abstract

Natural vision is dynamic: as an animal moves, its visual input changes dramatically. How can the visual system reliably extract local features from an input dominated by self-generated signals? In Drosophila, diverse local visual features are represented by a group of projection neurons with distinct tuning properties. Here we describe a connectome-based volumetric imaging strategy to measure visually evoked neural activity across this population. We show that local visual features are jointly represented across the population, and that a shared gain factor improves trial-to-trial coding fidelity. A subset of these neurons, tuned to small objects, is modulated by two independent signals associated with self-movement, a motor-related signal and a visual motion signal. These two inputs adjust the sensitivity of these feature detectors across the locomotor cycle, selectively reducing their gain during saccades and restoring it during intersaccadic intervals. This work reveals a strategy for reliable feature detection during locomotion. This dataset contains GCaMP6f and syt1GCaMP6f calcium responses from identified optic glomeruli in the Drosophila central brain, as well as walking behavior tracking data. It accompanies the manuscript Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, by MH Turner et al.

Methods

Visually evoked responses from optic glomeruli were extracted using the pan-glomerulus imaging and alignment approach described in Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila, by MH Turner et al. Briefly, raw imaging data were motion corrected and aligned to a connectome-based map of optic glomeruli in the central brain. Fluorescence signals from the corresponding voxels for each optic glomerulus were averaged to produce the optic glomerulus response trace. These response traces are included in .hdf5 datafiles that are accompanied by animal, experimental and stimulus metadata as well as stimulus timing information requried to align visual stimuli with fluorescence traces.

Usage Notes

The main analysis, modeling and figure generation code, which produced and can interact with the datafiles included in this dataset, can be found here.

Funding

National Institutes of Health, Award: K99-EY032549

National Institutes of Health, Award: F32-MH118707

National Institutes of Health, Award: R01-EY022638

National Institutes of Health, Award: R01-NS110060