Modular neuropile organization in the <i>Drosophila</i> larval brain facilitates identification and mapping of central neurons
Bibliographic record
Abstract
Elucidating how neuronal networks process information requires identification of critical individual neurons and their connectivity patterns. For this purpose, we used the third-instar Drosophila larval brain and applied reverse-genetic tools, immunolabeling procedures, and 3D digital reconstruction software. Consistent topological definition of neuropile compartments in the larval brain can be obtained through simple fluorescence-immunolabeling methods. The modular neuropiles can be used as a fiducial framework for mapping the projection patterns of individual neurons labeled with green fluorescent protein (GFP). GFP-labeled neurons often exhibit dendrite-like arbors as well as clustered varicose terminals on neurite branches that innervate identifiable neuropile compartments. We identified candidate cholinergic interneurons in genetic mosaic brains that overlap with the larval optic nerve terminus. By using the neuropile framework, we demonstrate that the candidate visual interneurons are not a subset of the previously identified circadian pacemaker neurons that also contact the larval optic nerve terminus; they may represent parallel pathways in the processing of visual inputs. Thus, in the Drosophila larval brain, modular neuropiles can be used as a framework for systematically identifying, mapping, and classifying interneurons; understanding their roles in behavior can then be pursued further.
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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 | 0.001 |
| 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".