Origins of proprioceptor feature selectivity and topographic maps in the <i>Drosophila</i> leg
Bibliographic record
Abstract
Abstract Our ability to sense and move our bodies relies on proprioceptors, sensory neurons that detect mechanical forces within the body. Proprioceptors are diverse: different subtypes detect different features of joint kinematics, such as position, directional movement, and vibration. However, because they are located within complex and dynamic peripheral tissues, the underlying mechanisms of proprioceptor feature selectivity remain poorly understood. Here, we investigate molecular and biomechanical contributions to proprioceptor diversity in the Drosophila leg. Using single-nucleus RNA sequencing, we found that different proprioceptor subtypes express similar complements of mechanosensory and other ion channels. However, anatomical reconstruction of the proprioceptive organ and connected tendons revealed major biomechanical differences between proprioceptor subtypes. We constructed a computational model of the proprioceptors and tendons, which identified a putative biomechanical mechanism for joint angle selectivity. The model also predicted the existence of a goniotopic map of joint angle among position-tuned proprioceptors, which we confirmed using calcium imaging. Our findings suggest that biomechanical specialization is a key determinant of proprioceptor feature selectivity in Drosophila . More broadly, our discovery of proprioceptive maps in the fly leg reveals common organizational principles between proprioception and other topographically organized sensory 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".