Effects of Synaptic and Myelin Plasticity on Learning in a Network of Kuramoto Phase Oscillators

TL;DR

This study explores how combined synaptic and myelin plasticity influence learning in a network of Kuramoto oscillators, revealing that myelin changes enable functional integration across structural clusters, which is crucial for understanding learning processes.

Contribution

It introduces a model integrating synaptic and myelin plasticity in Kuramoto oscillators, demonstrating their combined effect on network synchronization and learning.

Findings

Myelin plasticity enables functional communication across structural clusters.

Combined plasticity leads to different network states compared to synaptic plasticity alone.

Dynamic conduction velocity facilitates integration of segregated network modules.

Abstract

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 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. Hence, dynamic conduction velocity permits the functional integration of structurally segregated clusters. Our results confirm that network states following learning may be different when myelin plasticity is considered in addition to synaptic plasticity, pointing towards the relevance of integrating both factors in computational models of learning.