Rapid synaptic potentiation within the anterior cingulate cortex mediates trace fear learning
Bibliographic record
Abstract
Although the cortex has been extensively studied in long-term memory storage, less emphasis has been placed on immediate cortical contributions to fear memory formation. AMPA receptor plasticity is strongly implicated in learning and memory, and studies have identified calcium permeable AMPA receptors (CP-AMPARs) as mediators of synaptic strengthening. Trace fear learning engages the anterior cingulate cortex (ACC), but whether plastic events occur within the ACC in response to trace fear learning, and whether GluN2B subunits are required remains unknown. Here we show that the ACC is necessary for trace fear learning, and shows a rapid 20% upregulation of membrane AMPA receptor GluA1 subunits that is evident immediately after conditioning. Inhibition of NMDA receptor GluN2B subunits during training prevented the upregulation, and disrupted trace fear memory retrieval 48 h later. Furthermore, intra-ACC injections of the CP-AMPAR channel antagonist, 1-naphthylacetyl spermine (NASPM) immediately following trace fear conditioning blocked 24 h fear memory retrieval. Accordingly, whole cell patch clamp recordings from c-fos positive and c-fos negative neurons within the ACC in response to trace fear learning revealed an increased sensitivity to NASPM in recently activated neurons that was reversed by reconsolidation update extinction. Our results suggest that trace fear learning is mediated through rapid GluN2B dependent trafficking of CP-AMPARs, and present in vivo evidence that CP-AMPAR activity within the ACC immediately after conditioning is necessary for subsequent memory consolidation processes.
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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".