Temporal dynamics of neural ensemble coding of remembered target location in the primate prefrontal cortex
Bibliographic record
Abstract
Neurons in the primate lateral prefrontal cortex (LPFC) encode and maintain working memory (WM) representations in the absence of external stimuli. Neural computations underlying spatial WM in primates are traditionally studied using highly controlled tasks consisting of simple 2D visual stimuli and require a saccadic response. Hence, there is little known about how populations of LPFC neurons may maintain and transform 3D representations of space for animals to navigate towards remembered object locations. To explore this issue, we created a spatial WM task that takes place in a 3D virtual environment. This task presents a target in one of nine virtual locations. The subject is required to navigate to the remembered location by using a joystick after a two-second delay. Neural recordings were conducted in two male rhesus macaques using two 10×10 Utah arrays located in the LPFC (area 8A), resulting in 3847 neurons. We decoded target location on a single-trial basis using a novel high-efficiency classification technique. The method resulted in high decoding accuracy using a minimum number of neurons containing the highest target-specific information. In ensembles of 8-12 neurons, decoding accuracy ranged from 60%-90% (chance = ~11). We determined how neural ensembles encode and maintain information about target locations in three-dimensional space during each trial. Our results demonstrate that ensembles of 2-15 neurons in a group represent each of the nine selected targets that exist during the trial. Ensembles remain consistent over multiple trials of each session, and the specific target of each trial is decoded with 40% to 60% accuracy over chance. These results indicate that in addition to the information encoded in single-neuron activity, temporal dynamics of groups of neurons consistently interacting with each other is also informative and can be used for decoding.
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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".