Data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila

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.