Neural Ensembles in the Lateral Prefrontal Cortex Temporally Multiplex Task Features During Virtual Navigation
Bibliographic record
Abstract
Abstract Neuronal populations can expand their information encoding capacity using mixed selective neurons. This is particularly prominent in association areas such as the Lateral Prefrontal Cortex (LPFC) that integrate information from multiple sensory systems. During naturalistic conditions where subjects have agency, it is unclear how LPFC neuronal ensembles process space and time varying information. Here we show that during a virtual navigation task that requires associative memory and decision making, individual neurons and neuronal ensembles in the LPFC time-multiplex their selectivity for different task features to fluidly encode the temporal contingencies of the task and the animal’s choice. Neurons in ventral regions showed more selectivity for non-spatial features, while dorsal regions showed selectivity for space and eye movements. These results demonstrate that during naturalistic tasks with fluid scenery and spatiotemporal dynamics, LPFC neurons and neuronal ensembles time-multiplex the components of their selectivity, expanding the spatiotemporal capacity of their neural codes.
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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".