Responses of macaque V1 neurons to color images of natural scenes
Bibliographic record
Abstract
During natural vision, we scan scenes of a world full of colors with large and small eye movements. Surprisingly, the responses of cortical neurons are seldom studied under these conditions. Most commonly, gray scale images are displayed, and eye movements are mimicked by movie sequences that assume the eye is stationary during intersaccadic intervals. The results indicate that cortical activity is very low and sparse when viewing achromatic movies. We have recorded activity of V1 neurons while a monkey viewed calibrated color images of natural scenes from the McGill database and performed eye movement tasks. Eye position was recorded at high precision with a scleral search coil so that fixational saccades, drifts, and tremor were measureable. There was a wide range of response characteristics, but many neurons were continuously active during drift periods as well as immediately after saccades. This activity would be expected to contribute to the fine detailed vision that is enabled by fixational drift. However, it poses a challenge to determine whether the drift-related activity integrates easily into the rubric of sparse coding. When saccades were performed from a blank field to a natural image or vice-versa, we were able to separate the situations where the receptive field lands on a region of a natural scene or leaves it. We are currently investigating the balance of “on” and “off” responses that accompany these abrupt changes. Many of the neurons gave quite vigorous responses to colored images that were often greater than the response to the same image converted to gray scale. This comparison offers a novel measure of the contribution of color to cortical activity and the metabolic cost of this important perceptual capacity. Meeting abstract presented at VSS 2015
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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".