The Spatial Pattern of Response Magnitude and Selectivity for Orientation and Direction in Cat Visual Cortex
Bibliographic record
Abstract
Optical imaging studies of orientation and direction preference in visual cortex have typically used vector averaging to obtain angle and magnitude maps. This method has shown half-rotation orientation singularities (pinwheels) located within regions of low orientation vector magnitude. Direction preference is generally orthogonal to orientation preference, but often deviates from this, particularly in regions of low direction vector magnitude. Linear regions of rapid change in direction preference terminate in or near orientation singularities. The vector-averaging method is problematic however because it does not clearly disambiguate spatial variation in orientation tuning width from variation in height. It may also wrongly estimate preferred direction in regions where preference is weak. In this paper we analyze optical maps of cat visual cortex by fitting model tuning functions to the responses. This new method reveals features not previously evident. Orientation tuning height and width vary independently across the map: tuning height is always low near singularities, however regions of broad and narrow orientation tuning width can be found in regions of low tuning height, often alternating in a spoke-like fashion around singularities. Orientation and direction preference angles are always closely orthogonal. Reversals in direction preference form lines that originate precisely in orientation singularities.
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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 | 0.001 |
| 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 | 0.000 |
| 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".