A model-based framework for the analysis of miniature post-synaptic currents
Bibliographic record
Abstract
Miniature post-synaptic currents (mPSCs) have become a primary measure of synaptic modification during development, plasticity, and disease. ‘Minis’ represent the response of postsynaptic receptors to the spontaneous fusion of vesicles. They are a useful assay for the number and strength of synaptic connections, as mini event frequency is related to the number of functional release sites, and event amplitude is a measure of synapse strength [1]. Thus, accurate characterization of synapse dynamics relies critically on statistical analyses of event frequency and amplitude. We develop a new paradigm for mPSC analysis that uses likelihood methods [2] and formal goodness-of-fit assessments [3] to derive accurate statistical descriptions of their frequency and amplitude properties. In particular, we demonstrate that mPSC inter-event intervals and amplitudes within individual cells are well described by exponential and log-normal models. These characterizations allow us to analyze mPSCs at the single-cell level. We employ a parametric bootstrap based on these models to make accurate assessments of uncertainty within and between groups in the setting of small samples. This enables accurate estimation of responses for individual cells or groups, and paired comparisons of beforeand after- manipulations in single cells. We illustrate this approach in the analysis of excitatory mPSCs from acute slices of sensory midbrain. We show that the method may be broadly applicable to excitatory and inhibitory PSCs in other CNS regions, and is robust to changes in event selection parameters and recording conditions. Our method preserves information about the variability of events within individual cells and allows the summary of information across cells in order to make between-group comparisons. The use of an accurate model maximizes the efficiency of the resulting statistics, by taking advantage of the high degree of structure in the data. The framework allows accurate inferences to be made from studies of spontaneous activity, and for the first time extends analysis of synaptic function to the single cell level.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".