Working memory representations of visual motion direction are encoded in the firing patterns of neurons in dorsolateral prefrontal cortex, but not in area MT
Bibliographic record
Abstract
It is thought that primate dorsolateral prefrontal cortex (dlPFC) neurons play a role in the maintenance of visual information in working memory (Goldman-Rakic, 1995). It has been recently suggested that this process also involves the recruitment of neurons in early visual cortex that are selective for the stimulus features to be remembered. Supporting this hypothesis, recent fMRI studies have reported that the contents of visual working memory can be decoded from patterns of BOLD signals in visual areas (Harrison and Tong, 2009). However, because fMRI does not directly measure the neurons' spiking activity, it remains controversial whether this effect is attributable to variations in the firing patterns of neurons, or in the amplitude of other signals such as local field potentials. Here, we investigate this issue by recording the spiking activity of single neurons simultaneously from the dlPFC (n = 53) and early visual area MT (n = 33) of two rhesus monkeys during a working memory task requiring them to remember the motion direction of a sample random dot pattern and match it to the direction of one of two test patterns serially presented inside the neurons' receptive field. During the memory period, the activity of most dlPFC remained above or below baseline, and approximately 1/6 of the neurons showed sustained tuning to the remembered direction. In all of the recorded MT neurons, activity remained at baseline levels and direction tuning was not present throughout the memory period. Our results show that working memory representations of motion direction are encoded in the firing patterns of neurons in dlPFC but not in MT. They further suggest that the reported patterns of BOLD activation in visual cortex reflecting the contents of working memory may originate from changes in other signals such as local field potentials rather than spiking activity.
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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) | 0.000 | 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".