Interaction of NMDA receptors and L‐type calcium channels during early odor preference learning in rats
Bibliographic record
Abstract
Early odor preference learning in rats provides a simple model for studying learning and memory. Learning results in an enhanced output from mitral cells, which carry odor information from the olfactory bulb to the olfactory cortex. Mitral cell NMDA receptors (NMDARs) are critically involved in plasticity at the olfactory nerve to mitral cell synapse during odor learning. Here we provide evidence that L-type calcium channels (LTCCs) provide an additional and necessary source of calcium for learning induction. LTCCs are thought to act downstream of NMDARs to bridge synaptic activation and the transcription of the plasticity-related proteins necessary for 24-h learning and memory. Using immunohistochemistry, we have demonstrated that LTCCs are present in the mitral cell and are primarily located on mitral cell proximal dendrites in neonate rats. Behavioral experiments demonstrate that inhibiting the function of LTCCs via intrabulbar infusion of nimidopine successfully blocks learning induced by pairing isoproterenol infusion with odor, while activation of LTCCs via an intrabulbar infusion of BayK-8644 rescues isoproterenol-induced learning from a D-APV block. Interestingly, the infusion of BayK-8644 paired with odor is by itself not sufficient to induce learning. Synaptoneurosome Western blot and immunohistochemistry measurement of synapsin I phosphorylation following BayK-8644 infusion suggest LTCCs are involved in synaptic release. Finally, odor preference can be induced by gabazine disinhibition of mitral cells, and NMDAR opening is sufficient for the gabazine-induced learning. These results provide the first evidence that NMDARs and LTCCs interact to permit calcium-dependent mitral cell plasticity during early odor preference learning.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".