Spatial Relationships of Visuomotor Transformations in the Superior Colliculus Map
Bibliographic record
Abstract
The oculomotor system is well understood compared with other motor systems; however, we do not yet know the spatial details of sensory to motor transformations. This study addresses this issue by quantifying the spatial relationships between visual and motor responses in the superior colliculus (SC), a midbrain structure involved in the transformation of visual information into saccadic motor command signals. We collected extracellular single-unit recordings from 150 visual-motor (VM) and 28 motor (M) neurons in two monkeys trained to perform a nonpredictive visually guided saccade task to 110 possible target locations. Motor related discharge was greater than visual related discharge in 94% (141/150) of the VM neurons. Across the population of VM neurons, the mean locations of the peak visual and motor responses were spatially aligned. The visual response fields (RFs) were significantly smaller than and usually contained within the motor RFs. Converting RFs into the SC coordinate system significantly reduced any misalignment between peak visual and motor locations. RF size increased with increasing eccentricity in visual space but remained invariant on the SC map beyond 1 mm of the rostral pole. RF shape was significantly more symmetric in SC map coordinates compared with visual space coordinates. These results demonstrate that VM neurons specify the same location of a target stimulus in the visual field as the intended location of an upcoming saccade with minimal misalignment to downstream structures. The computational consequences of spatially transforming visual field coordinates to the SC map resulted in increased alignment and spatial symmetry during visual-sensory to saccadic-motor 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) | 0.001 | 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".