Vergence Neurons Identified in the Rostral Superior Colliculus Code Smooth Eye Movements in 3D Space
Bibliographic record
Abstract
The rostral superior colliculus (rSC) encodes position errors for multiple types of eye movements, including microsaccades, small saccades, smooth pursuit, and fixation. Here we address whether the rSC contributes to the development of neural signals that are suitable for controlling vergence eye movements. We use both single-unit recording and microstimulation techniques in monkey to answer this question. We found that vergence eye movements can be evoked using microstimulation in the rSC. Moreover, among the previously described neurons in rSC, we recorded a novel population of neurons that either increased (i.e., convergence neurons) or decreased (i.e., divergence neurons) their activity during vergence eye movements. In particular, these neurons dynamically encoded changes in vergence angle during vergence tracking, fixation in 3D space and the slow binocular realignment that occurs after disconjugate saccades, but were completely unresponsive during conjugate or the rapid component of disconjugate saccades (i.e., fast vergence) and conjugate smooth pursuit. Together, our microstimulation and single-neuron results suggest that the SC plays a role in the generation of signals required to precisely align the eyes toward targets in 3D space. We propose that accurate maintenance of 3D eye position, critical for the perception of stereopsis, may be mediated via the rSC.
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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".