Development of Single Retinofugal Axon Arbors in Normal and β2 Knock-Out Mice
Bibliographic record
Abstract
The maturation of retinal ganglion cell (RGC) axon projections in the dorsal lateral geniculate nucleus (dLGN) and the superior colliculus (SC) relies on both molecular and activity-dependent mechanisms. Despite the increasing popularity of the mouse as a mammalian visual system model, little is known in this species about the normal development of individual RGC axon arbors or the role of activity in this process. We used a novel in vivo single RGC labeling technique to quantitatively characterize the elaboration and refinement of RGC axon arbors in the dLGN and SC in wild-type (WT) and β2-nicotinic acetylcholine receptors mutant (β2(-/-)) mice, which have perturbed retinal waves, during the developmental period when eye-specific lamination and retinotopic refinement occurs. Our results suggest that eye-specific segregation and retinotopic refinement in WT mice are not the result of refinement of richly exuberant arbors but rather the elaboration of arbors prepositioned in the proper location combined with the elimination of inappropriately targeted sparse branches. We found that retinocollicular arbors mature ∼1 week earlier than retinogeniculate arbors, although RGC axons reach the dLGN and SC at roughly the same age. We also observed striking differences between contralateral and ipsilateral RGC axon arbors in the SC but not in the LGN. These data suggest a strong influence of target specific cues during arbor maturation. In β2(-/-) mice, we found that retinofugal single axon arbors are well ramified but enlarged, particularly in the SC, indicating that activity-dependent visual map development occurs through the refinement of individual RGC arbors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".