Visual circuitry for distance estimation in Drosophila
Bibliographic record
Abstract
Animals must infer the three-dimensional structure of their environment from two-dimensional retinal images. They use visual cues like motion parallax and binocular disparity to judge distances to objects, and studies across several animal models have found and characterized neural signals that correlate with visual distance. However, the causal role of these neurons in distance estimation and the range of their possible neural properties remain poorly understood. Here, we show that both directional and non-directional feature-selective neurons in the Drosophila visual system are involved in estimating distance during free locomotion. We used a high-throughput behavioral assay to perform a targeted silencing screen of visual neurons, and we subsequently characterized distance tuning using in vivo two-photon microscopy, thus linking distance perception directly to neural signals. Silencing the primary motion detectors eliminated distance-dependent behavior, consistent with reliance on motion parallax. Our screen also identified a visual feature-detecting neuron that encodes a non-canonical motion parallax signal: the signal is not direction selective for object or background motion, but it is tuned to the relative speeds of foreground and background, resulting in a signal that can measure relative distance. Our results demonstrate the behavioral roles of direction-selective and distance-tuned neurons in fly distance estimation and provide a framework for considering broader classes of neurons that encode distance through motion parallax.
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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".