Dynamic contrast change produces rapid gain control in visual cortex
Bibliographic record
Abstract
During normal vision, objects moving in the environment, our own body movements and our eye movements ensure that the receptive fields of visual neurons are being presented with continually changing contrasts. Thus, the visual input during normal behaviour differs from the type of stimuli traditionally used to study contrast coding, which are presented in a step-like manner with abrupt changes in contrast followed by prolonged exposure to a constant stimulus. The abrupt changes in contrast typically elicit brief periods of intense firing with low variability called onset transients. Onset transients provide the visual system with a powerful and reliable cue that the visual input has changed. In this paper we investigate visual processing in the primary visual cortex of cats in response to stimuli that change contrast dynamically. We show that 1-4 s presentations of dynamic increases and decreases in contrast can generate stronger contrast gain control than several minutes exposure to a stimulus of constant contrast. Thus, transient mechanisms of contrast coding are not only less variable than sustained responses but are also more rapid and flexible. Finally, we propose a quantitative model of contrast coding which accounts for changes in spike rate over time in response to dynamically changing image contrast.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".