Deciphering Cardiac Neural Network Dynamics: From In-Vivo Recordings to Unsupervised Spike Labeling
Bibliographic record
Abstract
The cardiac nervous system continuously modifies the heart's mechanical and electrical functions to meet the body's demand for blood circulation. This closed-loop control mechanism consists of interconnected neural populations that function collaboratively. It has evolved to manage different facets of cardiac function, resulting in cardiopulmonary signals such as blood pressure and respiration. In this work, we focus on the stellate ganglia, a bundle of sympathetic “fight-or-flight” nerves located in the front of the neck, which receives and integrates central, peripheral, and cardiopulmonary information to produce sympathetic responses in various disease pathologies. Using in-vivo extracellular microelectrode recordings, we investigate differences in information transfer between healthy porcines and porcines with chronic heart failure. We introduce two new metrics, cofluctuation and neural specificity, to describe the control of cardiac function by the stellate ganglion. Our analysis reveals the complex and dynamic nature of this control system and provides new insights into the neural mechanisms underlying cardiac function. Additionally, we discuss the challenges of spike sorting in our dataset and our approach to spike sorting as a subset of unsupervised learning.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".