Optical Imaging of Odor Preference Memory in the Rat Olfactory Bulb
Bibliographic record
Abstract
Early olfactory preference learning in rat pups occurs when novel odors are paired with reinforcing tactile stimulation that activate the noradrenergic locus coeruleus. Pairing of odor and a noradrenergic agonist in the olfactory bulb is both necessary and sufficient for odor preference learning. This suggests the memory change occurs in the olfactory bulb. Previous electrophysiological experiments demonstrated that odor preference training induces an increase in the field excitatory postsynaptic potential to olfactory nerve input and an alteration, after training, in glomerular [14C]2- deoxyglucose uptake and in single-unit responses of principal cells. We investigate here whether, 24 h after olfactory preference training, there is an alteration in intrinsic optical signals at the glomerular level. Six-day-old rat pups were trained, as previously, for a peppermint odor preference. Trained pups and control littermates were subjected to imaging of odor-induced intrinsic optical signals 1 day after the training session. Trained pups exhibited significantly larger responses to the peppermint compared with untrained littermates previously exposed to the same odor. The response of trained pups to a control odor (amyl acetate) was, however, not significantly different from that of untrained littermates. These observations demonstrate that odor preference memory can be read-out by optical imaging techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".