Control of cardiac function and noise from a decaying power spectrum
Bibliographic record
Abstract
Evidence is presented that adds to the debate surrounding the question: To what extent does neural control of cardiac output exploit noise? The transduction capability of cardiac afferent neurons, situated in and adjacent to the heart, is vital to feedback in control of cardiac function. An analysis of in situ cardiac afferent activity shows evidence of independent and exponentially distributed interspike intervals. An anatomical basis for such memoryless interspike intervals ultimately derives from the fact that each afferent neuron is associated with a field of sensory neurites, or bare nerve endings, that transduce local chemical and mechanical stimuli in a many-to-one fashion. As such, cardiac afferent neurons and their sensory neurite inputs are respectively modeled here by the Hodgkin-Huxley equations forced by "red" noise (decaying power spectrum) perturbing an otherwise constant subthreshold input. A variable barrier competition model is derived from these equations in order to address the question: How are noisy inputs being processed by sensory neurons to cause each spike? It is found that ion channels are responsible for significant input "whitening" (increased spectral power at higher frequency) through differentiation of the inputs. Such whitening is a means to distinguish low-frequency control signals from otherwise red noise fluctuations. Furthermore, spiking occurs when backward moving averages of the whitened inputs, over a window of the order of the sodium activation time scale, exceed an approximately constant barrier.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".