Neural sequences in primate prefrontal cortex encode working memory in naturalistic environments
Bibliographic record
Abstract
Working memory is the ability to briefly remember and manipulate information after it becomes unavailable to the senses. The mechanisms supporting working memory coding in the primate brain remain controversial. Here we demonstrate that microcircuits in layers 2/3 of the primate lateral prefrontal cortex dynamically represent memory content in a naturalistic task through sequential activation of single neurons. We simultaneously recorded the activity of hundreds of neurons in the lateral prefrontal cortex of macaque monkeys during a naturalistic visuospatial working memory task set in a virtual environment. We found that the sequential activation of single neurons encoded trajectories to target locations held in working memory. Neural sequences were not a mere successive activation of cells with memory fields at specific spatial locations, but an abstract representation of the subject’s trajectory to the target. Neural sequences were less correlated to target trajectories during perception and were not found during working memory tasks lacking the spatiotemporal structure of the naturalistic task. Finally, ketamine administration distorted neural sequences, selectively decreasing working memory performance. Our results indicate that neurons in the lateral prefrontal cortex causally encode working memory in naturalistic conditions via complex and temporally precise activation patterns.
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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".