Bibliographic record
Abstract
In addition to a topographic map of the retina, mammalian visual cortex contains superimposed, orderly periodic maps of features such as orientation, eye dominance, direction of motion and spatial frequency. There is evidence that these maps are overlaid so as to ensure that all combinations of the different parameters are represented as uniformly as possible across visual space. However, it is unknown to what extent geometrical factors limit the number of periodic maps which might simultaneously be present, given this constraint. This paper attempts to investigate the question by using a dimension reduction model to generate maps of simple, many- dimensional feature spaces onto a model two-dimensional cortex. The feature space included a model retina, plus N binary variables, corresponding to parameters such as ocular dominance or spatial frequency. The results suggest that geometrical factors do not sharply limit the ability of the cortex to represent combinations of parameters in spatially superimposed maps of similar periodicity. Considerations of uniform coverage suggest an upper limit of six or seven maps. A higher limit, of about nine or ten, may be imposed by the numbers of neurons (or minicolumns) available to represent each of 2(N) features within a given small region of cortex.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.001 |
| 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".