Bibliographic record
Abstract
Understanding the mechanisms by which long-term synaptic plasticity is expressed remains an important objective in neuroscience. From a physiological perspective, the strength of a synapse can be considered a consequence of several parameters including the probability that a presynaptic action potential (AP) evokes the release of neurotransmitter, the mean number of quanta of transmitter released when release is evoked, and the mean amplitude of a postsynaptic response to a single quantum. Various methods have been employed to estimate these quantal parameters from electrophysiological recordings; such "quantal analysis" has been used to support competing accounts of mechanisms of expression of long-term plasticity. Because electrophysiological recordings, even with minimal presynaptic stimulation, can reflect responses arising at multiple synaptic sites, these methods are open to alternative interpretations. By combining intracellular electrical recording with optical detection of transmission at individual synapses, however, it is possible to eliminate such ambiguity. Here, we describe methods for such combined optical and electrical monitoring of synaptic transmission in brain slice preparations and illustrate how quantal analyses thereby obtained permit more definitive conclusions about the physiological changes that underlie long-term synaptic plasticity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".