Computational protocol for modeling and analyzing synaptic dynamics using SRPlasticity
Bibliographic record
Abstract
Transient changes in synaptic strength, known as short-term plasticity (STP), play a fundamental role in neuronal communication. Here, we present a protocol for using SRPlasticity, a software package that implements a computational model of STP. SRPlasticity supports automatic characterization of electrophysiological data and simulation of synaptic responses. We describe steps for installing and utilizing SRPlasticity, preprocessing data, fitting models, and simulating responses. We then detail procedures for analyzing spike response plasticity (SRP) model parameters to infer functional groupings of STP. For complete details on the use and execution of this protocol, please refer to Rossbroich et al. 1 and Beninger et al. 2 • Steps for flexibly capturing synaptic dynamics using SRPlasticity software • Instructions for automated fitting to infer model parameters from experimental data • Guidance on predicting synaptic responses to novel presynaptic spike trains in silico • Procedures for visualization, clustering, and predicting classes Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Transient changes in synaptic strength, known as short-term plasticity (STP), play a fundamental role in neuronal communication. Here, we present a protocol for using SRPlasticity, a software package that implements a computational model of STP. SRPlasticity supports automatic characterization of electrophysiological data and simulation of synaptic responses. We describe steps for installing and utilizing SRPlasticity, preprocessing data, fitting models, and simulating responses. We then detail procedures for analyzing spike response plasticity (SRP) model parameters to infer functional groupings of STP.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.024 |
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".