Stochastic Synchronization and Coherence Resonance Near a Hopf Bifurcation
Bibliographic record
Abstract
<p>Noise is an inherent part of neuronal dynamics. Experimental studies have revealed that noise plays an important role in neural dynamics. It has been shown, for example, that noise can have a constructive effect on the functioning of biological systems such as noise-induced synchronization, which is typically studied in the context of excitable neural networks. Neural excitability and the bifurcations which result in the transition from quiescence to oscillation or bursting/spike emission largely determine the neurophysiological properties of neurons. For example, excitable neurons in the vicinity of a Hopf bifurcation have been shown to respond preferably to excitation and can be easily synchronized by a stochastic stimulus. In this thesis we study the roles of stochastic synchronization and coherence resonance in neuro-physiological models near a Hopf bifurcation. We begin by considering a mathematical model for a neural network in the vicinity of a Hopf bifurcation, where bursting can be induced by a stochastic stimulus. We show that the coherence of the network is optimized by an optimal level of stochastic stimulus. Then, we study the general class of such models by considering the canonical model for a normal form near a Hopf bifurcation. We show that synchronization is optimized by an optimal level of stochastic stimulus and may be further tuned by adjusting the coupling of our model in a non-trivial way.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".