Learning to be on time: temporal coordination of neural dynamics by activity-dependent myelination
Bibliographic record
Abstract
Abstract Activity-dependent myelination is the mechanism by which myelin changes as a function of neural activity, and plays a fundamental role in brain plasticity. Mediated by structural changes in glia, activity-dependent myelination regulates axonal conduction velocity. It remains unclear how neural activity impacts myelination to orchestrate the timing of neural signaling. We developed a model of spiking neurons enhanced with neuron-glia feedback. Inspired by experimental data and use-dependent synaptic plasticity, we introduced a learning rule, called the Activity-Dependent Myelination (ADM) rule, by which conduction velocity scales with firing rates. We found that the ADM rule implements a homeostatic control mechanism that promotes and preserves synchronization. ADM-mediated plasticity was found to optimize synchrony by compensating for variability in axonal lengths by scaling conduction velocity in an axon-specific way. This property was maintained even when the network structure is altered. We further explored how external stimuli interact with the ADM rule to trigger bidirectional and reversible changes in conduction delays. These results highlight the role played by activity-dependent myelination in synchronous neural communication and brain plasticity.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".