Competition is a driving force in topographic mapping
Bibliographic record
Abstract
Topographic maps are the primary means of relaying spatial information in the brain. Understanding the mechanisms by which they form has been a goal of experimental and theoretical neuroscientists for decades. The projection of the retina to the superior colliculus (SC)/tectum has been an important model used to show that graded molecular cues and patterned retinal activity are required for topographic map formation. Additionally, interaxon competition has been suggested to play a role in topographic map formation; however, this view has been recently challenged. Here we present experimental and computational evidence demonstrating that interaxon competition for target space is necessary to establish topography. To test this hypothesis experimentally, we determined the nature of the retinocollicular projection in Math5 (Atoh7) mutant mice, which have severely reduced numbers of retinal ganglion cell inputs into the SC. We find that in these mice, retinal axons project to the anteromedialj portion of the SC where repulsion from ephrin-A ligands is minimized and where their attraction to the midline is maximized. This observation is consistent with the chemoaffinity model that relies on axon-axon competition as a mapping mechanism. We conclude that chemical labels plus neural activity cannot alone specify the retinocollicular projection; instead axon-axon competition is necessary to create a map. Finally, we present a mathematical model for topographic mapping that incorporates molecular labels, neural activity, and axon competition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".