Distinct neural codes in primate Hippocampus and Lateral Prefrontal Cortex during associative learning in virtual environments
Bibliographic record
Abstract
Abstract The hippocampus (HPC) and the lateral prefrontal cortex (LPFC) are two cortical areas of the primate brain deemed essential to cognition. Here we hypothesize that the codes mediating neuronal communication in HPC and LPFC microcircuits have distinctively evolved to serve plasticity and memory function at different spatiotemporal scales. We used a virtual reality task in which animals navigated through a maze using a joystick and selected one of two targets in the arms of the maze according to a learned context-color rule. We found that neurons and neuronal populations in both regions encode similar information about the task. Moreover, we demonstrate that many HPC neurons concentrate spikes into bursts, whereas most layer II/III LPFC neurons sparsely distribute spikes over time. As the animals learned the task HPC neurons, but not LPFC neurons, increased their burst rate as a function of performance. When integrating spike rates over short intervals, HPC neuronal ensembles reached maximum decoded information with fewer neurons than LPFC ensembles. Our results show that during associative learning HPC principal cells concentrate spikes in bursts enabling temporal summation and fast synaptic plasticity in small populations of neurons and ultimately facilitating rapid encoding of associative memories. On the other hand, layers II/III LPFC pyramidal cells fire spikes more sparsely distributed in time and over a larger number of neurons. The latter would facilitate broadcasting of signals loaded in short term memory across neuronal populations without necessarily triggering fast synaptic plasticity.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".