A Normalization Circuit Underlying Coding of Spatial Attention in Primate Lateral Prefrontal Cortex
Bibliographic record
Abstract
Abstract Lateral prefrontal cortex (LPFC) neurons signal the allocation of voluntary attention; however, the neural computations underlying this function remain unknown. To investigate this, we recorded from neuronal ensembles in the LPFC of two Macaca fascicularis performing a visuospatial attention task. LPFC neural responses to a single stimulus were normalized when additional stimuli/distracters appeared across the visual field and were well-characterized by an averaging computation. Deploying attention toward an individual stimulus surrounded by distracters shifted neural activity from an averaging regime toward a regime similar to that when the attended stimulus was presented in isolation (winner-take-all; WTA). However, attentional modulation is both qualitatively and quantitatively dependent on a neuron’s visuospatial tuning. Our results show that during attentive vision, LPFC neuronal ensemble activity can be robustly read out by downstream areas to generate motor commands, and/or fed back into sensory areas to filter out distracter signals in favor of target signals.
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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) | 0.001 | 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".