Ensemble coding of context-dependent fear memory in the amygdala
Bibliographic record
Abstract
After fear conditioning, presenting the conditioned stimulus (CS) alone yields a context-specific extinction memory; fear is suppressed in the extinction context, but renews in any other context. The context-dependence of extinction is mediated by a brain circuit consisting of the hippocampus, prefrontal cortex (PFC) and amygdala. In the present work, we sought to determine at what level of this circuit context-dependent representations of the CS emerge. To explore this question, we used cellular compartment analysis of temporal activity by fluorescent in situ hybridization (catFISH). This method exploits the intracellular expression profile of the immediate early gene (IEG), Arc, to visualize neuronal activation patterns to two different behavioral experiences. Rats were fear conditioned in one context and extinguished in another; 24 h later, they were sequentially exposed to the CS in the extinction context and another context. Control rats were also tested in each context, but were never extinguished. We assessed Arc mRNA expression within the basal amygdala (BA), lateral amygdala (LA), ventral hippocampus (VH), prelimbic cortex (PL) and infralimbic cortex (IL). We observed that the sequential retention tests induced context-dependent patterns of Arc expression in the BA, LA, and IL of extinguished rats; this was not observed in non-extinguished controls. In general, non-extinguished animals had proportionately greater numbers of non-selective (double-labeled) neurons than extinguished animals. Collectively, these findings suggest that extinction learning results in pattern separation, particularly within the BA, in which unique neuronal ensembles represent fear memories after extinction.
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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".