Visual and motor signatures of locomotion dynamically shape a population code for feature detection in Drosophila
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.
-
Data from: Visual and motor signatures of locomotion dynamically shape a population code for feature detection in DrosophilaDryad Digital Repository, doi:10.5061/dryad.h44j0zpp8.
Article and author information
Author details
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
© 2022, Turner et al.
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.
Metrics
-
- 1,932
- views
-
- 250
- downloads
-
- 18
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
- Neuroscience
During delayed ballistic reaches, motor areas consistently display movement-specific activity patterns prior to movement onset. It is unclear why these patterns arise: while they have been proposed to seed an initial neural state from which the movement unfolds, recent experiments have uncovered the presence and necessity of ongoing inputs during movement, which may lessen the need for careful initialization. Here, we modeled the motor cortex as an input-driven dynamical system, and we asked what the optimal way to control this system to perform fast delayed reaches is. We find that delay-period inputs consistently arise in an optimally controlled model of M1. By studying a variety of network architectures, we could dissect and predict the situations in which it is beneficial for a network to prepare. Finally, we show that optimal input-driven control of neural dynamics gives rise to multiple phases of preparation during reach sequences, providing a novel explanation for experimentally observed features of monkey M1 activity in double reaching.
-
- Neuroscience
Humans make irrational decisions in the presence of irrelevant distractor options. There is little consensus on whether decision making is facilitated or impaired by the presence of a highly rewarding distractor, or whether the distractor effect operates at the level of options’ component attributes rather than at the level of their overall value. To reconcile different claims, we argue that it is important to consider the diversity of people’s styles of decision making and whether choice attributes are combined in an additive or multiplicative way. Employing a multi-laboratory dataset investigating the same experimental paradigm, we demonstrated that people used a mix of both approaches and the extent to which approach was used varied across individuals. Critically, we identified that this variability was correlated with the distractor effect during decision making. Individuals who tended to use a multiplicative approach to compute value, showed a positive distractor effect. In contrast, a negative distractor effect (divisive normalisation) was prominent in individuals tending towards an additive approach. Findings suggest that the distractor effect is related to how value is constructed, which in turn may be influenced by task and subject specificities. This concurs with recent behavioural and neuroscience findings that multiple distractor effects co-exist.