Bibliographic record
Abstract
Experimental results show that biological neural networks display spontaneous transitions between ordered and chaotic activity. However, in vitro measurements of the electrical signals fired by single neurons show a regular, reproducible dynamics, implying that the chaotic behavior is an emerging property of the network. A frequently adopted hypothesis is that chaos emerges as a consequence of a fine tuned balance between the excitatory and inhibitory neural connections in large networks with high connectivity. Here we argue that while this hypothesis is sufficient for explaining the emergence of complex behavior, it may not be necessary for explaining the observed spontaneous order-chaos transitions. More exactly, we show that simple neural oscillators can exhibit order-chaos transitions when forced by periodic signals generated by other neurons. In this model, the simplest such neural network consists of only three neurons connected in a master-slave architecture (one master and two slave neurons). We also show, both theoretically and using an electronic circuit model, that such a network can switch its dynamics between the regular and chaotic regimes by simply changing the firing signal frequency of the master (controlling) neuron. These results show that an increased network complexity is not necessary for explaining the spontaneous order-chaos transitions frequently observed in biological neural networks.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".