Functional architecture of visual responses in dorsal and ventral banks of anterior cingulate cortex
Bibliographic record
Abstract
Previous research in humans, macaques, and rodents has demonstrated a role of the medial frontal cortex in detecting errors, registering success, and exerting pro-active control on saccade production. The cortical circuitry accomplishing these computations is unknown. Here, we analyze neural spiking data from two monkeys collected using a linear electrode array to describe the functional properties of neurons across cortical layers in the dorsal and ventral banks of the caudal segments of anterior cingulate cortex (actually midcingulate cortex, MCC) during a visually-guided saccade countermanding task. Monkeys were rewarded for shifting gaze to a visual target unless, in infrequent random trials a stop signal appeared, which instructed the subject to cancel saccade initiation. Despite sampling over 900 neurons in MCC, less than 5% demonstrated significant modulation in response to a visual target. Typically, these responses were sustained, discharging until after saccade production. Around 70% of visually responsive neurons were most sensitive to a visual target appearing in one hemifield. Interestingly, as observed previously in SEF, MCC visual neurons showed an unexpected preference for ipsilateral visual stimuli. MCC visual neurons were modulated significantly later than those in occipital and temporal visual areas, as well as other frontal regions such as frontal and supplementary eye fields. Although we found no difference in visual onset latencies between the dorsal and ventral banks, task-related visual response latency varied across cortical layers. These findings provide the first report of the functional architecture of visual signals in two discrete regions of cingulate cortex and provide important constraints for microcircuit models of these areas.
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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".