Application of reverse correlation to the study of visual and extraretinal signals in the macaque superior colliculus
Bibliographic record
Abstract
The reverse correlation method has often been used to map the spatiotemporal receptive fields of visual neurons. For many visual neurons, the receptive fields are also modulated by extraretinal signals, including those related to attention, arousal, and eye movements. We have therefore adapted the reverse correlation method to characterize visual receptive fields in the context of saccadic eye movements. The technique was used to study neurons in the macaque superior colliculus, where pre-saccadic changes in the size and position of visual receptive fields have been reported previously (Wurtz and Mohler, 1976; Walker et al. 1995). Our stimulus was a sparse pattern of randomly-positioned black and white spots, presented against a gray background. The black and white spots occupied 2–5% of the display, and their positions were changed randomly at a frame rate of 85Hz. We mapped the receptive fields of neurons in the superficial and intermediate layers of the superior colliculus in one monkey during fixation as well as during visually guided saccades. We found that pooling over 700 to 1000 saccades provided in most cases a signal of sufficient strength to study pre-saccadic changes in receptive fields with a temporal resolution of around 50ms. In data from 85 neurons we found that many receptive fields changed their characteristics beginning about 100ms before saccades. In most cases an overall reduction of activity was observed, similar to the perceptual suppression of vision that occurs around the time of a saccade. Surprisingly, we did not observe any changes in receptive field position, even though such receptive field shifts are known to occur in the superior colliculus (Walker et al. 1995). We are currently probing this paradox to better delineate the advantages and disadvantages of reverse correlation for characterizing the interaction of extraretinal and visual signals in single neurons.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".