NMDA receptors in mouse anterior piriform cortex initialize early odor preference learning and L-type calcium channels engage for long-term memory
Bibliographic record
Abstract
The interactions of L-type calcium channels (LTCCs) and NMDA receptors (NMDARs) in memories are poorly understood. Here we investigated the specific roles of anterior piriform cortex (aPC) LTCCs and NMDARs in early odor preference memory in mice. Using calcium imaging in aPC slices, LTCC activation was shown to be dependent on NMDAR activation. Either D-APV (NMDAR antagonist) or nifedipine (LTCC antagonist) reduced somatic calcium transients in pyramidal cells evoked by lateral olfactory tract stimulation. However, nifedipine did not further reduce calcium in the presence of D-APV. In mice that underwent early odor preference training, blocking NMDARs in the aPC prevented short-term (3 hr) and long-term (24 hr) odor preference memory, and both memories were rescued when BayK-8644 (LTCC agonist) was co-infused. However, activating LTCCs in the absence of NMDARs resulted in loss of discrimination between the conditioned odor and a similar odor mixture at 3 hr. Elevated synaptic AMPAR expression at 3 hr was prevented by D-APV infusion but restored when LTCCs were directly activated, mirroring the behavioral outcomes. Blocking LTCCs prevented 24 hr memory and spared 3 hr memory. These results suggest that NMDARs mediate stimulus-specific encoding of odor memory while LTCCs mediate intracellular signaling leading to long-term 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) | 0.000 | 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) | 0.002 | 0.001 |
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".