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

Abstract
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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 associated with rotation of the animal. 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.

Data availability

All software and code is available on GitHub. Main analysis, modeling and figure generation code can be found here: https://github.com/mhturner/glom_pop; Visual stimulus code can be found here: https://github.com/ClandininLab/visanalysis and here: https://github.com/ClandininLab/flystim. Extracted ROI responses and associated stimulus metadata, along with raw imaging data, can be found in a Dryad repository here: https://doi.org/10.5061/dryad.h44j0zpp8.

The following data sets were generated

1. Turner MH
(2022) Data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila
Dryad Digital Repository, doi:10.5061/dryad.h44j0zpp8.

https://dx.doi.org/10.5061/dryad.h44j0zpp8

Article and author information

Author details

Maxwell H Turner

Department of Neurobiology, Stanford University, Stanford, United States

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-4164-9995
Avery Krieger

Department of Neurobiology, Stanford University, Stanford, United States

Competing interests
The authors declare that no competing interests exist.
Michelle M Pang

Department of Neurobiology, Stanford University, Stanford, United States

Competing interests
The authors declare that no competing interests exist.
Thomas R Clandinin

Department of Neurobiology, Stanford University, Stanford, United States

For correspondence
trc@stanford.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-6277-6849

Funding

National Institutes of Health (F32-MH118707)

Maxwell H Turner

National Institutes of Health (K99-EY032549)

Maxwell H Turner

National Institutes of Health (R01-EY022638)

Thomas R Clandinin

National Institutes of Health (R01NS110060)

Thomas R Clandinin

National Science Foundation (GRFP)

Avery Krieger

National Defense Science and Engineering Graduate (Fellowship)

Michelle M Pang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.