Characterizing the spatial organization of population codes in macaque prefrontal cortex during visuospatial tasks
Bibliographic record
Abstract
The lateral prefrontal cortex (LPFC) plays a key role in higher-order cognition. Electrophysiology studies in behaving animals show good decoding of task-relevant information from patterns of activity across neural populations in LPFC. However, functional magnetic resonance imaging (fMRI) studies in humans report only weak decoding of task-relevant information from LPFC activity. The limited access to prefrontal population codes in humans has hampered progress in understanding the neural computations that support human cognition. We hypothesize that the spatial topography of prefrontal population codes is too fine-grained to be effectively resolved by standard fMRI (2×2×2 mm3). We analyzed microelectrode array data (Utah array, 4×4 mm2, 10×10 channels spaced 0.4 mm apart) recorded from LPFC areas 8A and 9/46 of two macaques performing three visuospatial tasks: an oculomotor delayed response task, a visuospatial working memory task, and an associative learning task. For each monkey, task, and measurement session, we estimated each channel’s tuning profile by computing its mean firing rate for each condition. To assess the spatial scale of the population codes, we estimated tuning profile similarity between channel pairs as a function of distance, using global Moran’s I. To inspect the spatial topography of the population codes, we visualized channel tuning similarity on the arrays. We find that tuning profiles are spatially autocorrelated up to a distance of 3.5 mm across all tasks in most sessions for both monkeys. The array map visualizations suggest moderate spatial clustering of channels with similar tuning and a somewhat irregular topography. The observed topography and fine-grained spatial scale of the prefrontal population codes, which falls above the Nyquist frequency of standard fMRI sampling, may limit the sensitivity of standard fMRI to prefrontal population codes. High-field fMRI, with increased spatial resolution, may be able to bridge the gap between monkey electrophysiology and human fMRI.
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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".