神经突触和髓鞘可塑性对Kuramoto相位振荡器网络学习的影响
Effects of Synaptic and Myelin Plasticity on Learning in a Network of Kuramoto Phase Oscillators
摘要：
学习模型通常集中于突触可塑性。然而，学习是突触和髓鞘可塑性的结果。具体来说，突触变化通常与髓鞘变化共同发生并相互作用，导致这些过程之间复杂的动态相互作用。在这里，我们调查的影响，这些相互作用的耦合行为的系统仓本振荡器。为此，我们构建了一个完全连接的一维相位振荡器环形网络，其耦合强度（反映突触强度）和传导速度（反映髓鞘形成）均受赫布学习规则的调节。我们评估系统的结构（成对连接强度和传导速度）和功能连接（本地和全球同步行为）方面的行为。我们发现，在条件下，一个系统有限的突触可塑性发展两个不同的集群结构和功能，额外的适应性髓鞘允许跨这些结构集群的功能通信。因此，动态传导速度允许结构隔离的簇的功能整合。我们的研究结果证实，学习后的网络状态可能是不同的，髓鞘可塑性被认为是除了突触可塑性，指向的相关性整合两个因素的计算模型的学习。
英文摘要：
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.
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