Comparing Extraocular Motoneuron Discharges During Head-Restrained Saccades and Head-Unrestrained Gaze Shifts
Bibliographic record
Abstract
Burst neurons (BNs) in the paramedian pontine reticular formation provide the primary input to the extraocular motoneurons (MNs) during head-restrained saccades and combined eye-head gaze shifts. Prior studies have shown that BNs carry eye movement-related signals during saccades and carry head as well as eye movement-related signals during gaze shifts. Therefore MNs receive signals related to head motion during gaze shifts, yet they solely drive eye motion. Here we addressed whether the relationship between MN firing rates and eye movements is influenced by the additional premotor signals present during gaze shifts. Neurons in the abducens nucleus of monkeys were first studied during saccades made with the head stationary. We then recorded from the same neurons during voluntary combined eye-head gaze shifts. We conclude that the activity of MNs, in contrast to that of BNs, is related to eye motion by the same dynamic relationship during head-restrained saccades and head-unrestrained gaze shifts. In addition, we show that a standard metric-based analysis [i.e., counting the number of spikes (NOS) in a burst] yields misleading results when applied to the same data set. We argue that this latter approach fails because it does not properly consider the system's dynamics or the strong interactions between eye and head motion.
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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".