Embedded chimera states in recurrent neural networks
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Fully and partially synchronized brain activity plays a key role in normal cognition and in some neurological disorders, such as epilepsy. However, the mechanism by which synchrony and asynchrony co-exist in a population of neurons remains elusive. Chimera states, where synchrony and asynchrony coexist, have been documented only for precisely specified connectivity and network topologies. Here, we demonstrate how chimeras can emerge in recurrent neural networks by training the networks to display chimeras with machine learning. These solutions, which we refer to as embedded chimeras, are generically produced by recurrent neural networks with connectivity matrices only slightly perturbed from random networks. We also demonstrate that learning is robust to different biological constraints, such as the excitatory/inhibitory classification of neurons (Dale’s law), and the sparsity of connections in neural circuits. The recurrent neural networks can also be trained to switch chimera solutions: an input pulse can trigger the neural network to switch the synchronized and the unsynchronized groups of the embedded chimera, reminiscent of uni-hemispheric sleep in a variety of animals. Our results imply that the emergence of chimeras is quite generic at the meso- and macroscale suggesting their general relevance in neuroscience.
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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 | 0.002 | 0.001 |
| 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 it