Spike count correlations between primate dorsolateral prefrontal cortex neurons during a spatial working memory task
Bibliographic record
Abstract
It has been shown that neurons in the primate prefrontal cortex encode information during the delay period of working memory tasks for spatial locations, however it remains unclear whether and how these units interact with each other. We examined this question by recording single cell responses from microelectrode arrays (each array = 96 microelectrodes, Blackrock Inc., UT) implanted in dorsolateral prefrontal cortex (area 8v) of two macaca fascicularis during a working memory task, and measuring spike count correlations between the recorded units. During trials the animals fixated on a central spot for 494 to 800 ms, then a circular sine wave grating was presented at one of 16 randomly selected locations for 507 ms. The offset of the grating was followed by a delay period that could last between 494 and 2000 ms and ended with the offset of the central fixation point. This latter event instructed the animals to make a saccade to the remembered location of the previously presented grating. The spiking activity of neurons was simultaneously recorded in blocks of 32 channels and sorted using Plexon software (Plexon Inc, TX). We recorded from 20 well isolated single units for a total of 190 neuronal pairs. We found that spike rate correlations between the pairs computed across all target locations were significantly higher during the delay period (r = 0.13) than during the stimulus presentation period (r = 0.04) (p < 0.01, paired t-test). Our results suggest that interactions between neurons in the dlPFC increase during working memory maintenance in the absence of visual inputs. Such interactions may be critical for working memory encoding by neuronal populations within the area.
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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) | 0.000 | 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".