Modeling neuronal ensemble firing activity through intermittent Chaos
Bibliographic record
Abstract
While a large body of experimental works provided results about stimulus evoked activities in primary sensory cortices, the functional role and the dynamics of spontaneous activity (SA) have been less widely addressed. From the dynamical perspective, a major open problem is represented by the evolution of SA, most modeling works tacitly assuming SA activity to be essentially random. The alternative possibility that SA is, at least for a significant part, deterministic, although highly chaotic, did not gain much attention. We addressed the problem by a novel synthetic approach. We first classified the multi-unit spike patterns into a reasonable number of classes. Through the use of symbolic dynamics we described SA by characterizing its long range correlations and maximum residence times for a pattern class. We found that we could exploit the nonlinear dynamics of a logistic map tuned in the region of Type I Intermittency. We finally applied our analyses on recordings taken both from normal and neuropathic rats and we found a great variety of behaviors. Some data exhibited complex dynamics. Others were more regular with intermittent-like phases constituted by long reiteration of the same pattern class. By combining a random noisy component with a logistic map we could generate class sequences that faithfully reproduced the long range correlations and maximum residence times measured on the dataset.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".