Dynamic Characterization of Agonist and Antagonist Oculomotoneurons During Conjugate and Disconjugate Eye Movements
Bibliographic record
Abstract
In this report, we provide the first quantitative characterization of the relationship between the spike train dynamics of medial rectus oculomotoneurons (OMNs) and eye movements during conjugate and disconjugate saccades. We show that a simple, first-order model (i.e., containing eye position and velocity terms) provided an adequate model of neural discharges during both on and off-directed conjugate saccades, while a second-order model, which included a decaying slide term, significantly improved the ability to fit neuronal responses by approximately 10% (P<0.05). To understand how the same neurons drove disconjugate eye movements, we evaluated whether sensitivities estimated during conjugate saccades could be used to predict responses during disconjugate saccades. For the majority of neurons (68%), a conjugate-based model failed, and instead neurons preferentially encoded the position and velocity of the ipsilateral eye. Similar to our previous results with abducens motoneurons, we also found that position and velocity sensitivities of OMNs decreased with increasing velocity, and the simulated population drive of OMNs during disconjugate saccades was less (approximately 10%) than during conjugate saccades. Taken together, our results provide evidence that the activation of the antagonist, as well as agonist, motoneuron pools must be considered to understand the neural control of horizontal eye movements across different oculomotor behaviors. Moreover, we propose that the undersampling of smaller motoneurons (e.g., nontwitch) was likely to account for the missing drive observed during disconjugate saccades; these cells are thought to be more specialized for vergence movements and thus could provide the additional input required to command disconjugate eye movements.
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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".