Fast computation of multivariate synchrony index in sliding windows: application to cardiac neurons
Bibliographic record
Abstract
Multielectrode array neuronal recordings in atrial ganglionated plexi are characterized by low firing rates, marked non-stationarity, interplay with the cardiovascular and pulmonary systems and artifacts generated by myocardial activity, which creates challenges very different from brain recordings. To explore population dynamics of intrinsic cardiac neurons, a jitter-based synchrony index has been defined to quantify pairwise synchrony between neurons. In this paper, we extend this synchrony index to multiple time series in order to monitor global (multivariate) synchrony. Numerical techniques are developed to efficiently compute synchrony indices and their statistical significance in a large number of time windows. A scaletime graphical representation is proposed to visualize synchrony in sliding windows of varying lengths. This approach is validated in synthetic time series and in experimental data sets recorded in 11 dogs. Results show the ability of the method to monitor synchrony over time in neuron populations, between neurons and the cardiopulmonary system and between neuron firing and electrical stimulation. These tools will facilitate the exploration and robust quantitative analysis of multiple-hour recordings in cardiac ganglionated plexi to efficiently identify relevant periods of activity in relation to physiological or external stimuli and cardiac arrhythmia.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".