Perforant path inputs to hippocampal subfields predict heterogeneous AMPA receptor subunit expression following rapid new spatial learning in a novel context
Bibliographic record
Abstract
The hippocampus plays a critical role in spatial learning and memory. Its contribution to support these kinds of learning and memory functions relies on synaptic plasticity and related molecular mechanisms, well documented in the long-term potentiation (LTP) literature. The present experiment measures AMPA subunit expression, in a ratio of GluA2:GluA1 as an indicator of plasticity across the hippocampus, in rats that underwent new spatial learning in either a familiar or novel context. Statistically significant effects in this plasticity indicator were observed of context condition, time after task and hippocampal subfield. Based on the strong inputs of entorhinal cortex to hippocampus, we also identified differences in GluA2:GluA1 expression trends between time points and room conditions that mirror trends in medial and lateral entorhinal cortex connectivity between new room and same room context learning, respectively. Across the transverse axis in infrapyramidal dentate gyrus, CA3 and CA1, plasticity followed entorhinal cortex projection patterns. Along the transverse axis in the suprapyramidal blade of the dentate gyrus, and along the long axis in dentate gyrus and CA3, results did not follow entorhinal cortex subregion projection patterns. These latter results may be indicative of pattern separation in the dentate gyrus and emotional triage functions of the ventral hippocampus.
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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 | 0.000 |
| 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".