Timing Determines Tuning: a Rapid Spatiotemporal Transformation in Superior Colliculus Neurons During Reactive Gaze Shifts
Bibliographic record
Abstract
Abstract Gaze saccades –rapid shifts of the eyes and head toward a goal— have provided fundamental insights into the neural control of movement. For example, it has been shown that the superior colliculus (SC) transforms a visual target (T) code to future gaze (G) location commands after a memory delay. However, this transformation has not been observed in ‘reactive’ saccades made directly to a stimulus, so its contribution to normal gaze behavior is unclear. Here, we tested this using a quantitative measure of the spatial continuum between T and G coding based on variable gaze errors. We demonstrate that a rapid T-G transformation occurs between SC visual and motor responses during reactive saccades, even within visuomotor cells, with a continuous spatiotemporal shift in coding occurring in cell types (visual, visuomotor, motor). We further show that the primary determinant of this spatial code was not the intrinsic visual-motor index of different cells or populations, but rather the timing of the response in all cells. These results suggest that the SC provides a rapid spatiotemporal transformation for normal gaze saccades, that its motor responses contribute to variable gaze errors, and that those errors arise from a noisy spatiotemporal transformation involving all SC neurons. Significance Statement Oculomotor studies have demonstrated visuomotor transformations in structures like the superior colliculus with the use of trained behavioral manipulations, like the memory delay and antisaccades tasks, but it is not known how this happens during normal saccades. Here, using a spatiotemporal model fitting method based on endogenous gaze errors in ‘reactive’ gaze saccades, we show that the superior colliculus provides a rapid spatiotemporal transformation from target to gaze coding that involves visual, visuomotor, and motor neurons. This technique demonstrates that SC spatial codes are not fixed, and may provide a quantitative biomarker for assessing the health of sensorimotor transformations.
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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) | 0.000 | 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".