Effects of synaptic and myelin plasticity on learning in a network of Kuramoto phase oscillators

Models of learning typically focus on synaptic plasticity. However, learning is the result of both synaptic and myelin plasticity. Specifically, synaptic changes often co-occur and interact with myelin changes, leading to complex dynamic interactions between these processes. Here, we investigate the implications of these interactions for the coupling behavior of a system of Kuramoto oscillators. To that end, we construct a fully connected, one-dimensional ring network of phase oscillators whose coupling strength (reflecting synaptic strength) as well as conduction velocity (reflecting myelination) are each regulated by a Hebbian learning rule. We evaluate the behavior of the system in terms of structural (pairwise connection strength and conduction velocity) and functional connectivity (local and global synchronization behavior). We find that adaptive myelination is able to both functionally decouple structurally connected oscillators as well as to functionally couple structurally disconnected oscillators. With regard to the latter, we find that for conditions in which a system limited to synaptic plasticity develops two distinct clusters both structurally and functionally, additional adaptive myelination allows for functional communication across these structural clusters. These results confirm that network states following learning may be different when myelin plasticity is considered in addition to synaptic plasticity, pointing toward the relevance of integrating both factors in computational models of learning.