Orientation specificity of contrast adaptation in mouse primary visual cortex
Bibliographic record
Abstract
Contrast adaptation is a commonly studied phenomenon in vision, where prolonged exposure to spatial contrast alters perceived stimulus contrast and produces characteristic shifts in the contrast response functions of primary visual cortex neurons in cats and primates. In this study we investigated contrast adaptation in mouse primary visual cortex with two goals in mind. First, we sought to establish a quantitative description of contrast adaptation in an animal model, where genetic tools are more readily applicable to this phenomenon. Second, the orientation specificity of contrast adaptation was studied to comparatively assess the possible role of local cortical networks in contrast adaptation. In cats and primates, predictable differences in visual processing across the cortical surface are thought to be caused by inhomogeneous local network membership that arises from the pinwheel organization of orientation columns. Because mice lack this pinwheel organization, we predicted that local cortical networks would have access to a broad spectrum of orientation signals, and contrast adaptation in mice would not be specific to the recorded cell's preferred orientation. We found that most mouse V1 neurons showed contrast adaptation that was robust regardless of whether the adapting stimulus matched the cell's preferred orientation or was orthogonal to it.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".