Effects of Blocking Multiple Sources of Calcium in Hippocampus During Spatial Learning and Memory Using a Rapid Acquisition Variant of the Morris Water Task
Bibliographic record
Abstract
Long-term potentiation (LTP) is proposed to be the molecular mechanism underlying learning and memory in the brain. A key event for LTP is the influx of calcium into post-synaptic neurons via multiple ion channel control systems. One such system involves N-methyl-D-aspartate receptors (NMDARs), which were originally believed to be essential for LTP and new learning. Recent studies have demonstrated that hippocampal NMDARs are critical for learning new spatial information in a novel environment; however, when pre-training occurs prior to new spatial learning, these receptors are not needed. Additionally, researchers have shown that activation of voltage-gated calcium channels (VGCCs) and their associated calcium influx can induce LTP independent of NMDARs. These findings led to the idea that the amount of calcium required for learning in hippocampus depends on whether the new learning takes place in a novel or familiar environment, with a novel environment demanding greater calcium influx. It was hypothesized that to impair new learning in a familiar environment both NMDARs and VGCCs would need to be blocked. Long-Evans rats were trained in a three-phase version of the Morris water task, which included pre-training, new learning mass-training, and a probe test. Prior to mass-training, intrahippocampal VGCCs were blocked individually or in combination with NMDARs blockade to evaluate their effects on the rats learning and memory. The results showed that blocking both NMDARs and VGCCs simultaneously impaired new spatial learning with familiar information, whereas VGCC blockade alone did not.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".