Laminar organization of the superior colliculus priority map
Bibliographic record
Abstract
The superior colliculus (SC) is a multilayered midbrain structure with depth-dependent cortical/subcortical connectivity, and a longstanding role in the control of attention/gaze. While the superficial layers (SCs) have been associated with a bottom-up saliency map, the intermediate layers (SCi) have been described as a priority map, where neuronal signals related to visual salience and behavioral relevance combine to determine attention/gaze. However, the use of single electrodes to understand SC laminar function has been a major limitation due to inaccurate depth estimates. Here, we examined depth-dependent processing of stimuli of different salience/relevance across the intermediate and deeper SC layers using a linear microelectrode (LMA; 16ch, 200 μm inter-contact spacing). Rhesus monkeys were presented with an array of oriented color stimuli (~200 items) with two salient but feature-distinct oddballs. One oddball was goal-relevant (salient/relevant), the other goal-irrelevant (salient/non-relevant), and both were embedded in a feature-homogenous array of ‘distractors’ (non-salient/non-relevant). Following array onset, the animals maintained fixation for 0.5–0.7s allowing temporal separation between visual- and saccade- processes. The fixation stimulus then disappeared, instructing the animal to saccade to the goal-relevant oddball for a reward. We examined multiunit activity (MUA), local field potential activity (LFP), and current source density (CSD). We observed a depth-specific change from net inward-to-outward current flow in the saccade-evoked LFP, and corresponding CSD, indicating a depth-dependent functional distinction. We also observed depth-dependent oddball selectivity corresponding roughly to the upper window (~1600 μm) defined by the CSD cutoff, consistent with SCi. This oddball response was maximal at the center of the window and systematically attenuated dorsal and ventral from this. The deepest sites showed visual and saccadic responses, yet were not oddball selective. These results are consistent with a depth-dependent priority map in the SCi that combines information about stimulus saliency and relevancy to systematically rank order map locations for attention/gaze control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".