A critical contribution of a sparse neuronal ensemble in the amygdala central nucleus to extinction
Bibliographic record
Abstract
Adaptive behaviour critically depends on the delicate and dynamic balance between acquisition and extinction memories. Disruption of this balance, particularly when the extinction memory loses control over behaviour, is the root of treatment failure of maladaptive behaviours such as substance abuse or anxiety disorders. Understanding this balance requires a better understanding of the underlying neurobiology and its contribution to behavioural regulation. Here, we used Daun02 in Fos-lacZ transgenic rats to delete extinction-recruited neuronal ensembles in BLA and CN and examine their contribution to behaviour in an appetitive Pavlovian task. Deletion of extinction-activated ensembles in CN but not BLA impaired the retrieval of extinction and increased activity in the BLA. The disruptive effect of deleting these CN ensembles was enduring as it hindered further extinction learning, and promoted greater levels of behavioural restoration across opposing levels of the response scale seen in spontaneous recovery and reinstatement. Our data indicate that the initial extinction-recruited CN ensemble is critical to the acquisition-extinction balance, and that greater behavioural restoration does not mean weaker extinction contribution. These findings provide a novel avenue for thinking about the neural mechanisms of extinction and in developing treatments for cue-triggered appetitive behaviours.
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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 | 0.001 |
| 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".