Increasing Acetylcholine Levels in the Hippocampus or Entorhinal Cortex Reverses the Impairing Effects of Septal GABA Receptor Activation on Spontaneous Alternation
Bibliographic record
Abstract
Intra-septal infusions of the gamma-aminobutyric acid (GABA) agonist muscimol impair learning and memory in a variety of tasks. This experiment determined whether hippocampal or entorhinal infusions of the acetylcholinesterase inhibitor physostigmine would reverse such impairing effects on spontaneous alternation performance, a measure of spatial working memory. Male Sprague-Dawley rats were given intra-septal infusions of vehicle or muscimol (1 nmole/0.5 microL) combined with unilateral intra-hippocampal or intra-entorhinal infusions of vehicle or physostigmine (10 microg/microL for the hippocampus; 7.5 microg/microL or 1.875 microg/0.25 microL for the entorhinal cortex). Fifteen minutes later, spontaneous alternation performance was assessed. The results indicated that intra-septal infusions of muscimol significantly decreased percentage-of-alternation scores, whereas intra-hippocampal or intra-entorhinal infusions of physostigmine had no effect. More importantly, intra-hippocampal or intra-entorhinal infusions of physostigmine, at doses that did not influence performance when administered alone, completely reversed the impairing effects of the muscimol infusions. These findings indicate that increasing cholinergic levels in the hippocampus or entorhinal cortex is sufficient to reverse the impairing effects of septal GABA receptor activation and support the hypothesis that the impairing effects of septal GABAergic activity involve cholinergic processes in the hippocampus and the entorhinal cortex.
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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) | 0.001 | 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".