The stability of behavioral synchronization in a network of bursting neurons: a new explanation for epileptogenesis
Bibliographic record
Abstract
In this paper, we explored a new category of synchronization, namely the "behavioral synchronization". Instead of describing the signal, this category refers to the behavior. Two systems can be, each of them, in function mode A or B. If both of them are simultaneously in the same mode A or B, they are in synchronized behavior state. However, the outputs of the systems can be uncorrelated. The model proposed for investigation is a network of neurons that generate bursting (firing) behavior. The individual neuron displays characteristic firing patterns determined by the number and kind of ion channels in its membrane. One of the neuroscience problems is to explain how the system's dynamics depend on the properties of individual neurons, the synaptic architecture by which they are connected, and the strength and time course of the synaptic connections. In our model proposed for investigation, the output signals are chaotic and uncorrelated, although the systems are behaviorally synchronized. We proposed a new explanation for the appearance of the epileptic seizures that uses the concept of behavioral synchronization and is included in the family of hypothesis that state that the epilepsy is a network disorder. We proved mathematically and by simulations that, for a large range of values for the control parameters, the network is unstable and has chaotic behavior
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".