Odor preference learning and memory modify GluA1 phosphorylation and GluA1 distribution in the neonate rat olfactory bulb: Testing the AMPA receptor hypothesis in an appetitive learning model
Bibliographic record
Abstract
An increase in synaptic AMPA receptors is hypothesized to mediate learning and memory. AMPA receptor increases have been reported in aversive learning models, although it is not clear if they are seen with memory maintenance. Here we examine AMPA receptor changes in a cAMP/PKA/CREB-dependent appetitive learning model: odor preference learning in the neonate rat. Rat pups were given a single pairing of peppermint and 2 mg/kg isoproterenol, which produces a 24-h, but not a 48-h, peppermint preference in the 7-d-old rat pup. GluA1 PKA-dependent phosphorylation peaked 10 min after the 10-min training trial and returned to baseline within 90 min. At 24 h, GluA1 subunits did not change overall but were significantly increased in synaptoneurosomes, consistent with increased membrane insertion. Immunohistochemistry revealed a significant increase in GluA1 subunits in olfactory bulb glomeruli, the targets of olfactory nerve axons. Glomerular increases were seen at 3 and 24 h after odor exposure in trained pups, but not in control pups. GluA1 increases were not seen as early as 10 min after training and were no longer observed 48 h after training when odor preference is no longer expressed behaviorally. Thus, the pattern of increased GluA1 membrane expression closely follows the memory timeline. Further, blocking GluA1 insertion using an interference peptide derived from the carboxyl tail of the GluA1 subunit inhibited 24 h odor preference memory providing causative support for our hypothesis. PKA-mediated GluA1 phosphorylation and later GluA1 insertion could, conjointly, provide increased AMPA function to support both short-term and long-term appetitive memory.
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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".