Reactivation of encoding ensembles in the prelimbic cortex supports temporal associations
Bibliographic record
Abstract
Fear conditioning is encoded by strengthening synaptic connections between the neurons activated by a conditioned stimulus (CS) and those activated by an unconditioned stimulus (US), forming a memory engram, which is reactivated during memory retrieval. In temporal associations, activity within the prelimbic cortex (PL) plays a role in sustaining a short-term, transient memory of the CS, which is associated with the US after a temporal gap. However, it is unknown whether the PL has only a temporary role, transiently representing the CS, or is part of the neuronal ensembles that support the retrieval, i.e., whether PL neurons support both transient, short-term memories and stable, long-term memories. We investigated neuronal ensembles underlying temporal associations using fear conditioning with a 5-s interval between the CS and US (CFC-5s). Controls were trained in contextual fear conditioning (CFC), in which the CS-US overlaps. We used Robust Activity Marking (RAM) to selectively manipulate PL neurons activated by CFC-5s learning and Targeted Recombination in Active Populations (TRAP2) mice to label neurons activated by CFC-5s learning and reactivated by memory retrieval in the amygdala, medial prefrontal cortex, hippocampus, perirhinal cortices (PER) and subiculum. We also computed their co-reactivation to generate correlation-based networks. The optogenetic reactivation or silencing of PL encoding ensembles either promoted or impaired the retrieval of CFC-5s but not CFC. CFC-5s retrieval reactivated encoding ensembles in the PL, PER, and basolateral amygdala. The engram network of CFC-5s had higher amygdala and PER centralities and interconnectivity. The same PL neurons support learning and stable associative memories.
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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) | 0.001 | 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".