Dorsal hippocampus is necessary for novel learning but sufficient for subsequent similar learning
Bibliographic record
Abstract
Our current understanding of brain mechanisms involved in learning and memory has been derived largely from studies using experimentally naïve animals. However, it is becoming increasingly clear that not all identified mechanisms may generalize to subsequent learning. For example, N-methyl-D-aspartate glutamate (NMDA) receptors in the dorsal hippocampus are required for contextual fear conditioning in naïve animals but not in animals previously trained in a similar task. Here we investigated how animals learn contextual fear conditioning for a second time-a response which is not due to habituation or generalization. We found that dorsal hippocampus infusions of voltage-dependent calcium channel blockers or the inhibitory neurotransmitter gamma-aminobutyric acid (GABA) agonist impaired the first, not the second contextual learning. Only manipulations of the entire hippocampus led to an impairment in second learning. Specifically, inactivation of either the dorsal or ventral hippocampus caused the remaining portion of the hippocampus to acquire and consolidate the second learning. Thus, dorsal hippocampus seems necessary for initial contextual fear conditioning, but either the dorsal or ventral hippocampus is sufficient for subsequent conditioning in a different context. Together, these findings suggest that prior training experiences can change how the hippocampus processes subsequent similar learning.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".