Olfactory threat extinction in the piriform cortex: An age-dependent employment of NMDA receptor-dependent long-term depression
Bibliographic record
Abstract
Extinction of threat memory is a measure of behavioral flexibility. In the absence of additional reinforcement, the extinction of learned behaviors allows animals and humans to adapt to their changing environment. Extinction mechanisms and their therapeutic implications for maladaptive learning have been extensively studied. However, how aging affects extinction learning is much less understood. Using a rat model of olfactory threat extinction, we show that the extinction of olfactory threat memory is impaired in aged Sprague-Darley rats. Following extinction training, long-term depression (LTD) in the piriform cortex (PC) was inducible ex vivo in aged rats and was NMDA receptor (NMDAR)-independent. On the other hand, adult rats acquired successful olfactory threat extinction, and LTD was not inducible following extinction training. Neuronal cFos activation in the posterior PC correlated with learning and extinction performance in rats. NMDAR blockade either systemically or locally in the PC during extinction training prevented successful extinction in adult rats, following which NMDAR-dependent LTD became inducible ex vivo. This suggests that extinction learning employs NMDAR-dependent LTD mechanisms in the PC of adult rats, thus occluding further LTD induction ex vivo. The rescue of olfactory threat extinction in aged rats by D-cycloserine, a partial NMDAR agonist, suggests that the impairment in olfactory threat extinction of aged animals may relate to NMDAR hypofunctioning and a lack of NMDAR-dependent LTD. These findings are consistent with an age-related switch from NMDAR-dependent to NMDAR-independent LTD in the PC. Optimizing NMDAR function in sensory cortices may improve learning and flexible behavior in the aged population.
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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) | 0.001 | 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".