Superior colliculus (SC) neural activity codes visually guided head-unrestrained gaze movements in retinal coordinates
Bibliographic record
Abstract
Neural activity in the SC is highly correlated with gaze shifts composed of both eye and head movements (Freedman and Sparks, 1996; Munoz et al. 1991). Further, SC stimulation evokes eye and head gaze shifts that converge as a function of amplitude and initial gaze position, consistent with an eye-fixed motor code for gaze (Klier et al. 2001). We hypothesize that SC neural activity correlates best with gaze target location in retinal coordinates. This predicts that the optimal directional tuning of SC neurons will change as a specific function of amplitude tuning and initial gaze position (Smith and Crawford 2005). Electrical stimulation and/or visual receptive field examination are being used to estimate the optimal gaze amplitude and direction for each recording location. Monkeys randomly fixate one of three different initial gaze directions each separated by 20+/−10 degrees for 500 ms and then make their head-free gaze shift to one of five visual targets placed along a semi-circle of iso-amplitude targets (centered around the position of the receptive field maximum for straight-ahead gaze). We have recorded from eighty one SC neurons, sixty of these have been fully tested in the head-unrestrained paradigm. To date, analysis of 21 neurons shows that SC neurons do indeed show strong initial gaze position dependent firing changes during head-unrestrained gaze shifts. These responses cluster around the theoretical curve for an eye-fixed retinal code, as opposed to a fixed-vector or spatially-fixed coding scheme. Furthermore, some SC neurons show a position-dependent modulation of their firing rates as a function of the initial gaze position, as required for a non-linear transformation of retinal coordinates into gaze motor coordinates (Smith and Crawford 2005).
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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".