Myelin is built by oligodendrocytes, specialized glial cells that each form multiple sheaths around several axons. During development, oligodendrocyte precursor cells migrate through the central nervous system and mature, generating a dynamic period of sheath formation and loss followed by pruning. Neural activity shapes this process: increased firing enhances precursor proliferation and maturation, boosting myelination. For a long time, mature myelin was considered essentially stable, changing only when new oligodendrocytes appeared or in disease. More recent experimental evidence has overturned that view, showing that myelination remains dynamic throughout adulthood. Studies in the rodent cortex reveal that learning, experience, and behavior alter sheath length, number, thickness, and organization, and there is even growing evidence for activity-dependent myelin retraction. Neurons and oligodendrocytes form a bidirectional feedback loop that shapes the myelination program of the brain in real time.

The functional significance of this loop is profound. By remodeling myelin, oligodendrocytes tune axonal conduction velocity and thereby the delays with which action potentials arrive at their postsynaptic targets. This introduces a timing-based plasticity mechanism that operates in parallel to changes in synaptic strength, supporting learning, memory, and sensory and motor function. In the healthy brain, ADM optimizes and coordinates neural communication. But abnormal activity can hijack it. In certain forms of epilepsy, seizure activity promotes maladaptive hypermyelination that reinforces seizure patterns and drives disease progression, a feedback loop of dysfunction. In demyelinating disorders such as multiple sclerosis, compromised myelination underlies the sensory, motor, and cognitive deficits patients experience. Despite this significance, ADM lacks the strong theoretical framework that synaptic plasticity enjoys, largely because it involves multiple mechanisms interacting across widely different time scales: action potentials propagate in milliseconds, whereas myelin changes unfold over hours to days, and the relevant processes span subcellular signaling, single axons, gray matter microcircuits, and large-scale white matter networks.

Nguyen and Lefebvre organize the emerging computational literature into three classes of models, each operating at a distinct spatial scale. The first class focuses on individual axons, where neuron-glia interactions occur, and examines how changes in myelin structure map onto changes in conduction timing. Because structural remodeling in vivo often alters multiple myelin properties simultaneously, computational models can isolate the impact of individual variables and determine where causal relationships exist. These models are often biophysically detailed, explicitly representing action potential dynamics along myelinated axons composed of sheath and node compartments, frequently relying on Hodgkin-Huxley-like kinetics to simulate realistic ionic currents.

A landmark example comes from combined experimental-computational work by Ford and colleagues, who fit their model to geometrical measurements and electrophysiological recordings from gerbil auditory fibers. Building on the Halter and Clark multi-compartment model of the myelinated axon, which independently models intra-axonal, peri-axonal, and extra-axonal potentials, they established how the ratio between internode length and axon diameter influences conduction velocity. The smaller ratio observed in low-frequency auditory fibers alone accounts for a 15 percent increase in conduction velocity, and both axon types exhibit an optimal ratio that maximizes speed: internodes that are too short lack overall myelination, while depolarizations are not efficiently transferred between nodes when internodes are too long. Follow-up modeling of node length, fit to rat optic nerve and cortex measurements, revealed that with a constant number of ion channels per node, increasing node length almost linearly decreases conduction velocity due to increased capacitance and axial resistance, yet with constant channel density there is an optimal node length. Micrometer-scale changes in node length can increase conduction velocity by up to 20 percent in the optic nerve, and nodal remodeling presents a more metabolically efficient strategy for tuning conduction than altering internode architecture.

Axonal models have also examined how spatial heterogeneity in myelin coverage impacts signaling, injury susceptibility, and recovery. Myelin sheaths vary in length and are often non-uniformly arranged along axons, a patchy organization that is difficult to precisely manipulate in vivo but easy to control in simulation. Work by Scurfield and Latimer showed that, assuming the same number of remyelinated segments, transitions between short and long internodes slow conduction more than if all shorter internodes occupy one half of the axon and all longer ones the other. More recent modeling by Talidou and Lefebvre, using mouse imaging data to generate physiologically realistic sheath lengths and arrangements, demonstrated that axons with uniform myelin show negligible variability in conduction delays, whereas heterogeneous motifs show high, nonlinear, path-dependent variability, and that spatial heterogeneity increases the likelihood of conduction failure, making some motifs more vulnerable to demyelination. The major limitation of this class of models is that they do not explicitly model ADM; they treat myelin microstructure as a static parameter and model different myelination states as a proxy for its outcome. Still, they provide evidence that specific structural configurations can optimize spike timing, which is a primary goal of ADM.

The second class operates at the microcircuit level, where conduction velocity serves as a proxy for myelination and the feedback between neural activity and myelin adaptation is modeled directly. Talidou and colleagues built a recurrent network of excitatory spiking neurons in which conduction delays are state-dependent, using a Hebbian, use-dependent rule in which conduction velocity scales with firing rate along a given axon. Simulations showed that ADM acts as a homeostatic mechanism of gain control, temporally coordinating action potentials and up-regulating correlated activity, and that it can bidirectionally and reversibly alter conduction velocities in response to external stimuli, suggesting myelin plasticity responds dynamically to different brain states such as learning versus rest. Because synaptic connectivity was fixed, the results highlight how timing plasticity can complement synaptic plasticity in maintaining optimal brain function.

Pajevic and colleagues took a step further by asking how oligodendrocytes might integrate local spike timing as feedback to tune conduction velocities. In their framework, each oligodendrocyte releases a transient global intracellular signaling factor in response to action potentials propagating along any of the multiple axons it myelinates, while each axon carries a unique local myelination factor whose release is proportional to the global signal. This combination of global and local signals allows selective, bidirectional modulation of myelination along each axon. Model simulations reveal that oligodendrocytes can actively tune conduction velocities to selectively synchronize correlated spike trains while leaving uncorrelated trains unaffected, with synchronization greatest when firing rates are 10 Hz or less and transient responses span 10 to 40 milliseconds. This proposes a biologically plausible mechanism in which oligodendrocytes act as coincidence detectors of neural activity. The limitations are clear, however: existing microcircuit models rely on phenomenological learning rules, bypass explicit myelin structure, consider only excitatory networks, and compress the timescales of myelination for computational tractability.

The third class addresses the whole brain. Most inter-regional traffic flows through white matter, a heavily myelinated fiber network occupying roughly half of the mammalian brain volume, and large-scale models adopt phenomenological neural mass formulations that link directly to neuroimaging data such as fMRI, EEG, and MEG. Pajevic and colleagues emphasized that myelin-dependent changes in conduction velocity alter time delays, which can strongly influence brain oscillations and synchrony, drawing on the phenomenon of amplitude death, whereby dispersed delays suppress oscillatory activity. Using time-delayed Kuramoto oscillator networks, they showed that millisecond-scale changes in delays can impede the ability of neural assemblies to synchronize, and that delays directly alter oscillation frequency. Noori and colleagues then implemented a phenomenological learning rule in which conduction velocity along each axon changes according to the oscillatory phase difference between nodes, informed by macaque white matter structural connectivity. Their simulations revealed that ADM enables an initially incoherent network to self-regulate conduction velocities and achieve a globally phase-locked state, compensating for differences in tract lengths to maintain similar delays, and that plastic velocities enable synchronization at lower synaptic strengths and across wider frequency bands, even restoring synchrony after perturbations or injury. Park and Lefebvre confirmed mathematically that phase-dependent delays increase the stability of globally synchronous states and promote resilience to the loss of axonal connections.

Toward a unified framework, Nguyen and Lefebvre argue that the field remains fragmented and that future progress depends on interdisciplinary iteration between models and experiments. The physiological mechanisms underlying ADM remain unclear, and current models change myelination based on firing rate, individual spike timings, or synchrony, while ignoring oligodendrocyte heterogeneity and the contributions of astrocytes, precursor cells, and microglia. A particularly promising mechanistic candidate is calcium-dependent signaling: recent in vivo imaging shows that increases in intracellular calcium transients within developing oligodendrocytes trigger myelin sheath elongation, while sheath shortening is associated with lower transient frequency, suggesting a calcium-mediated feedback loop that could temporally bridge fast neural activity and slower myelin changes. Multiscale modeling offers a path across spatial scales, as illustrated by recent work linking a biophysically detailed axonal model of rhesus monkey prefrontal neurons to a spiking network showing that demyelination-induced propagation failures impair working memory but can be restored with remyelination. The authors conclude that future models should include dynamic conduction delays, analogous to the widespread adoption of variable synaptic weights, and that, like synaptic plasticity models, ADM models may clarify this form of plasticity in healthy and pathological brain states while inspiring new applications in engineering, machine learning, and artificial intelligence.

Subject of Research: Neuroscience

Article Title: A computational roadmap to activity-dependent myelination

Article References: Nguyen, T., & Lefebvre, J. (2026). A computational roadmap to activity-dependent myelination. Frontiers in Computational Neuroscience, 20, Article 1895171. https://doi.org/10.3389/fncom.2026.1895171

Image Credits: AI Generated

DOI: 10.3389/fncom.2026.1895171

Keywords: activity-dependent myelination, myelin plasticity, oligodendrocytes, conduction velocity, neural synchrony, computational neuroscience, white matter, brain plasticity, computational, roadmap, activity-dependent, myelination