Approaches and Limitations in the Investigation of Synaptic Transmission and Plasticity
Bibliographic record
Abstract
The numbers and strengths of synapses in the brain change throughout development, and even into adulthood, as synaptic inputs are added, eliminated, and refined in response to ongoing neural activity. A number of experimental techniques can assess these changes, including single-cell electrophysiological recording which offers measurements of synaptic inputs with high temporal resolution. Coupled with electrical stimulation, photoactivatable opsins, and caged compounds, to facilitate fine spatiotemporal control over release of neurotransmitters, electrophysiological recordings allow for precise dissection of presynaptic and postsynaptic mechanisms of action. Here, we discuss the strengths and pitfalls of various techniques commonly used to analyze synapses, including miniature excitatory/inhibitory (E/I) postsynaptic currents, evoked release, and optogenetic stimulation. Together, these techniques can provide multiple lines of convergent evidence to generate meaningful insight into the emergence of circuit connectivity and maturation. A full understanding of potential caveats and alternative explanations for findings is essential to avoid data misinterpretation.
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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 | 0.063 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| 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".