Competition between Retinal Ganglion Axons for Targets under the Servomechanism Model Explains Abnormal Retinocollicular Projection of Eph Receptor-Overexpressing or Ephrin-Lacking Mice
Bibliographic record
Abstract
Topographic mapping of retinal ganglion axons to the midbrain is computed by the servomechanism model, which is based on the experimental result of cell attachment. Cells expressing a certain level of Eph proteins (receptors for ephrin ligands) optimally attach to a surface that expresses a specific level of ephrin ligand density. The retina has an increasing nasal-to-temporal gradient of Eph receptor density, and the optic tectum/superior colliculus has an increasing rostral-to-caudal gradient of membrane-bound ephrin ligand. An axon from the retina has an identification tag of a certain level of Eph receptor density depending on its retinal position and adheres to the site on the tectum/superior colliculus expressing ephrin ligands at a critical ligand density level. Quantitatively, a retinal axon has a receptor density (R) that is determined by its retinal position, and the axon terminal is induced to adhere to the tectal site of ligand density (L = S/R), where S is a constant. Consequently, the servomechanism model defines positions of axon terminals on the midbrain. Abnormal topographic maps are reported in a knock-in experiment with elevated density of Eph receptors and a knock-out experiment lacking ephrin ligands using gene-targeting technology. By adding competition between axon terminals for target sites to the servomechanism model, the abnormal maps became easy to understand. Furthermore, the servomechanism-competition model allowed conjecture of the gradient shapes of receptor and ligand densities and estimation of the capacity of the midbrain surface to accept retinal axon terminals.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".