<i>Visual circuitry for distance estimation in</i> Drosophila
Bibliographic record
Abstract
Abstract Animals must infer the three-dimensional structure of their environment from two-dimensional images on their retinas. In particular, visual cues like motion parallax and binocular disparity can be used to judge distances to objects. Studies across several animal models have found neural signals that correlate with visual distance, but the causal role of these neurons in distance estimation as well as the range of possible neural properties that can inform distance estimation have remained poorly understood. Here, we developed a novel high-throughput behavioral assay to identify neurons in the Drosophila visual system that are involved in distance estimation during free locomotion. We found that silencing the primary motion detectors in the fly visual system eliminated their ability to perceive distance, consistent with a reliance on motion parallax to judge distance. Through a targeted silencing screen of visual neurons during behavior and through in vivo two-photon microscopy, we identified a visual projection neuron that encodes the parallax signal in the relative motion of foreground and background. Interestingly, it differs from previously identified parallax-tuned neurons in its lack of direction selectivity both to moving bars and to moving backgrounds. This non-canonical tuning is interpretable in the context of parallax signals that the fly would likely encounter during naturalistic walking behavior. Our results demonstrate how both direction selective and non-direction selective feature-detecting neurons can contribute to distance estimation using parallax cues, providing a framework for considering broader classes of parallax-encoding neurons in distance estimation across visual systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".