Functional Specialization and Distributed Processing across Marmoset Lateral Prefrontal Subregions
Bibliographic record
Abstract
Abstract A prominent aspect of the organization of primate lateral prefrontal cortex (lPFC) is its division into a number of cytoarchitecturally distinct subregions. Investigations in macaque lPFC using neurophysiological approaches have provided much insight into the functions associated with these subregions; however, our understanding is based largely on a patchwork of findings from many studies and across many animals, rarely covering the entire lPFC in individual subjects. Here, we leveraged the small size and lissencephalic cortex of the common marmoset ( Callithrix jacchus ) to characterize the responses of large numbers of single lPFC neurons to a diverse collection of test stimuli recorded across sets of lPFC subregions using high-density microelectrode arrays. Untethered extracellular electrophysiological recordings were obtained from two adult marmosets with 4 x 4 mm 96-channel Utah arrays implanted in lPFC, covering areas 8aD, 8aV, 9, 10, 46D, 46V and 47. We employed a test battery comprised of a variety of visual stimuli including faces and body parts, auditory stimuli including marmoset calls, and a spatial working memory task. Task-modulated units and units responsive to different stimulus modalities were distributed throughout the lPFC. Visual, auditory and call-selective units were distributed across all lPFC subregions. Neurons with contralateral visual receptive fields were found in 8aV and 8aD. Neurons responsive to faces and saccade-related units were found in 8aV, 8aD, 10, 46V and 47. These findings demonstrate that responses to some stimuli are relatively restricted within specific lPFC subregions, while others are more distributed throughout the marmoset lPFC.
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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 | 0.000 |
| 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".