Hippocampal long-term depression is required for the consolidation of spatial memory
Bibliographic record
Abstract
Although NMDA receptor (NMDAR)-dependent long-term potentiation (LTP) and long-term depression (LTD) of glutamatergic transmission are candidate mechanisms for long-term spatial memory, the precise contributions of LTP and LTD remain poorly understood. Here, we report that LTP and LTD in the hippocampal CA1 region of freely moving adult rats were prevented by NMDAR 2A (GluN2A) and 2B subunit (GluN2B) preferential antagonists, respectively. These results strongly suggest that NMDAR subtype preferential antagonists are appropriate tools to probe the roles of LTP and LTD in spatial memory. Using a Morris water maze task, the LTP-blocking GluN2A antagonist had no significant effect on any aspect of performance, whereas the LTD-blocking GluN2B antagonist impaired spatial memory consolidation. Moreover, similar spatial memory deficits were induced by inhibiting the expression of LTD with intrahippocampal infusion of a short peptide that specifically interferes with AMPA receptor endocytosis. Taken together, our findings support a functional requirement of hippocampal CA1 LTD in the consolidation of long-term spatial 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 | 0.000 |
| 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".