A Specific Role of Hippocampal NMDA Receptors and Arc Protein in Rapid Encoding of Novel Environmental Representations and a More General Long-Term Consolidation Function
Bibliographic record
Abstract
Activation of the NMDA receptor (NMDAR) has been proposed to be a key event responsible for the structural changes that occur in neurons during learning and memory formation. It has been extensively studied yet no consensus has been reached on its mnemonic role as both NMDAR dependent and independent forms of learning have been observed. We investigated the role that hippocampal NMDAR have in rapid spatial learning and memory across training environments. Hippocampal NMDAR was blocked via intra-hippocampal injection of the competitive antagonist CPP. Groups of rats were pre-trained on a spatial version of the Morris water task, and then mass reversal training under NMDAR blockade occurred in the same or different training environments as pre-training. We measured expression of Arc protein throughout the main hippocampal subfields, CA1, CA3, and dentate gyrus, after mass-training. We observed that NMDAR blockade allowed for rapid spatial learning, but not consolidation, when the SUBJECTS used previously acquired environmental information. Interestingly, NMDAR blockade impaired rapid spatial learning when rats were mass-trained in a novel context. Arc protein expression in the dentate gyrus followed this pattern of NMDAR dependent spatial behavior, with high levels of expression observed after being trained in the new environment, and low levels when trained in the same environment. CPP significantly reduced Arc expression in the dentate gyrus. These results implicate dentate NMDAR in the acquisition of novel environmental information.
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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".