Bibliographic record
Abstract
One niche of experimental biology that has experienced considerable progress is the neurobiology of learning and memory. A key contributor to such progress has been the widespread use of transgenic and 'knockout' mice to elucidate the mechanisms of identifiable phenotypes of learning and memory. Inbred mouse strains are needed to generate genetically modified mice. However, genetic variations between inbred strains can confound the interpretation of cellular neurophysiological phenotypes of mutant mice. It is known that altered physiological strength of synaptic transmission ('synaptic plasticity') can modify and regulate learning and memory. Characterization of the synaptic phenotypes of inbred mouse strains is needed to identify the most appropriate strains to use for generating mutant mouse models of memory function. More importantly, comparative electrophysiological analyses of inbred mice per se can also shed light on which forms of synaptic plasticity underlie particular types of learning and memory. Many such analyses have focused on synaptic plasticity in the hippocampus because of the critical roles of this brain structure in the formation and consolidation of long-term memories. Comparative electrophysiological data obtained from several inbred mouse strains are reviewed here to highlight the following key notions: (1) synaptic plasticity is influenced by the genetic backgrounds of inbred mice; (2) the plasticity of hippocampal synapses in inbred mice is ;tuned' to particular temporal patterns of activity; (3) long-term potentiation, but not long-term depression, is a cellular correlate of behavioural memory performance in some strains; (4) synaptic phenotyping of inbred mouse strains can identify cellular models of memory impairment that can be used to elucidate mechanisms that may cause specific memory deficits.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".