Karimian M, Dibenedetto D, Moerel M, Burwick T, Westra R L, De Weerd P, Senden M
Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, 6229 ER Maastricht, The Netherlands.
Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD Maastricht, The Netherlands.
Chaos. 2019 Aug;29(8):083122. doi: 10.1063/1.5092786.
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.
学习模型通常聚焦于突触可塑性。然而，学习是突触可塑性和髓鞘可塑性共同作用的结果。具体而言，突触变化常常与髓鞘变化同时发生并相互作用，从而导致这些过程之间产生复杂的动态交互。在此，我们研究这些交互作用对Kuramoto振子系统耦合行为的影响。为此，我们构建了一个全连接的一维环形相位振子网络，其耦合强度（反映突触强度）以及传导速度（反映髓鞘形成）均由赫布学习规则进行调节。我们从结构（成对连接强度和传导速度）和功能连接性（局部和全局同步行为）方面评估系统的行为。我们发现，适应性髓鞘形成既能在功能上使结构相连的振子解耦，也能在功能上使结构不相连的振子耦合。关于后者，我们发现，对于一个仅限于突触可塑性的系统在结构和功能上都形成两个不同集群的情况，额外的适应性髓鞘形成能够实现跨这些结构集群的功能通信。这些结果证实，当除了考虑突触可塑性之外还考虑髓鞘可塑性时，学习后的网络状态可能会有所不同，这表明在学习的计算模型中整合这两个因素具有重要意义。
