A computational roadmap to activity-dependent myelination

There is growing evidence to support myelin plasticity as a complementary mechanism to synaptic plasticity in the brain. Activity-dependent myelination (ADM) enables oligodendrocytes to modify myelin structure in response to neural activity, modulating axonal conduction velocity and delays to maintain precise signal timing. This introduces a timing-based plasticity mechanism that operates in parallel to changes in signal strength. Despite its significance, ADM lacks a strong theoretical framework due to the interaction of multiple mechanisms operating on widely different time scales. Computational models are beginning to address this gap by providing powerful tools to interpret and guide experimental studies. This perspective explores the growing number of computational ADM models, comparing how they link neural activity to myelin adaptation, and their assumptions, strengths, and limitations. We also discuss the challenges of modeling ADM and open questions, positioning ADM as a key component of neural plasticity.