Naturalistic coding of working memory in primate prefrontal cortex
Bibliographic record
Abstract
Summary The primate lateral prefrontal cortex (LPFC) is considered fundamental for temporarily maintaining and manipulating mental representations that serve behavior, a cognitive function known as working memory 1 . Studies in non-human primates have shown that LPFC lesions impair working memory 2 and that LPFC neuronal activity encodes working memory representations 3 . However, such studies have used simple displays and constrained gaze while subjects held information in working memory 3 , which put into question their ethological validity 4,5 . Currently, it remains unclear whether LPFC microcircuits can support working memory function during natural behavior. We tested macaque monkeys in a working memory navigation task in a life-like virtual environment while their gaze was unconstrained. We show that LPFC neuronal populations robustly encode working memory representations in these conditions. Furthermore, low doses of the NMDA receptor antagonist, ketamine, impaired working memory performance while sparing perceptual and motor skills. Ketamine decreased the firing of narrow spiking inhibitory interneurons and increased the firing of broad spiking cells reducing population decoding accuracy for remembered locations. Our results show that primate LPFC generates robust neural codes for working memory in naturalistic settings and that such codes rely upon a fine balance between the activation of excitatory and inhibitory neurons.
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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".