Olfactory Bulb Glomerular NMDA Receptors Mediate Olfactory Nerve Potentiation and Odor Preference Learning in the Neonate Rat
Bibliographic record
Abstract
Rat pup odor preference learning follows pairing of bulbar beta-adrenoceptor activation with olfactory input. We hypothesize that NMDA receptor (NMDAR)-mediated olfactory input to mitral cells is enhanced during training, such that increased calcium facilitates and shapes the critical cAMP pattern. Here, we demonstrate, in vitro, that olfactory nerve stimulation, at sniffing frequencies, paired with beta-adrenoceptor activation, potentiates olfactory nerve-evoked mitral cell firing. This potentiation is blocked by a NMDAR antagonist and by increased inhibition. Glomerular disinhibition also induces NMDAR-sensitive potentiation. In vivo, in parallel, behavioral learning is prevented by glomerular infusion of an NMDAR antagonist or a GABA(A) receptor agonist. A glomerular GABA(A) receptor antagonist paired with odor can induce NMDAR-dependent learning. The NMDA GluN1 subunit is phosphorylated in odor-specific glomeruli within 5 min of training suggesting early activation, and enhanced calcium entry, during acquisition. The GluN1 subunit is down-regulated 3 h after learning; and at 24 h post-training the GluN2B subunit is down-regulated. These events may assist memory stability. Ex vivo experiments using bulbs from trained rat pups reveal an increase in the AMPA/NMDA EPSC ratio post-training, consistent with an increase in AMPA receptor insertion and/or the decrease in NMDAR subunits. These results support a model of a cAMP/NMDA interaction in generating rat pup odor preference learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".