Myelin-induced gain control in nonlinear neural networks
Bibliographic record
Abstract
Myelin surrounds axonal membranes to increase the conduction velocity of nerve impulses and thus reduce communication delays in neural signaling. Changes in myelination alter the distribution of delays in neural circuits, but the implications for their operation are poorly understood. We present a joint computational and non-linear dynamical method to explain how myelin-induced changes in axonal conduction velocity impact the firing rate statistics and spectral response properties of recurrent neural networks. Using a network of spiking neurons with distributed conduction delays driven by a spatially homogeneous noise, we combined probabilistic and mean field approaches. These reveal that myelin implements a gain control mechanism while stabilizing neural dynamics away from oscillatory regimes. The effect of myelin-induced changes in conduction velocity on network dynamics was found to be more pronounced in presence of correlated stochastic stimuli. Further, computational and theoretical power spectral analyses reveal a paradoxical effect where the loss of myelin promotes oscillatory responses to broadband time-varying stimuli. Taken together, our findings show that myelination can play a fundamental role in neural computation and its impairment in myelin pathologies such as epilepsy and multiple sclerosis. Myelin accelerates neural signaling by increasing axonal conduction velocity, but its impact on network dynamics remains unclear. The authors show that myelination implements a gain control mechanism, stabilizing neural dynamics away from oscillatory regimes, offering insights into its role in neural computation and disease.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".