P.133 Neurons in the lateral prefrontal cortex encode task features during virtual navigation
Bibliographic record
Abstract
Background: The lateral prefrontal cortex (LPFC) is uniquely found in primates and has been associated with contextual learning. This function is thought to be subserved by neurons that are tuned to abstract concepts and the combination of those concepts. LPFC neuron tuning remains to be fully investigated in naturalistic conditions. Methods: Two macaques were trained to perform a context-colour association task while using a joystick to navigate in an X-shaped maze. They were implanted with two 96-channel microelectrode arrays, targeting the LPFC. Mean firing rates were computed and multivariate linear regressions were used to determine tuning. Results: LPFC neurons were tuned to context (12.4%), color position (6.2%), target side (17.2%), and were selective to more than one feature (21.2%). LPFC neurons acquired tuning to task features in an ordered manner, starting with context (130.1±27.4ms), followed by the colour position (296.2±21.4ms) and then target side (493.3±19.3ms). Furthermore, most neurons (54%) changed their tuning over time. Conclusions: We demonstrate that single neurons can encode relevant features embedded in a naturalistic virtual environment. Our results support previous observations that LPFC neurons combine individual features and suggest that these features are also combined temporally. These findings contribute towards understanding the LPFC and have potential practical implications.
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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) | 0.002 | 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".