Khoshkhou Mahsa, Montakhab Afshin
Department of Physics, College of Sciences, Shiraz University, Shiraz, Iran.
Front Comput Neurosci. 2018 Aug 14;12:59. doi: 10.3389/fncom.2018.00059. eCollection 2018.
Despite their significant functional roles, beta-band oscillations are least understood. Synchronization in neuronal networks have attracted much attention in recent years with the main focus on transition type. Whether one obtains explosive transition or a continuous transition is an important feature of the neuronal network which can depend on network structure as well as synaptic types. In this study we consider the effect of synaptic interaction (electrical and chemical) as well as structural connectivity on synchronization transition in network models of Izhikevich neurons which spike regularly with beta rhythms. We find a wide range of behavior including continuous transition, explosive transition, as well as lack of global order. The stronger electrical synapses are more conducive to synchronization and can even lead to explosive synchronization. The key network element which determines the order of transition is found to be the clustering coefficient and not the small world effect, or the existence of hubs in a network. These results are in contrast to previous results which use phase oscillator models such as the Kuramoto model. Furthermore, we show that the patterns of synchronization changes when one goes to the gamma band. We attribute such a change to the change in the refractory period of Izhikevich neurons which changes significantly with frequency.
尽管β波段振荡具有重要的功能作用，但其却最不为人所了解。近年来，神经网络中的同步现象备受关注，主要聚焦于转变类型。一个人获得的是爆发性转变还是连续性转变，是神经网络的一个重要特征，它可能取决于网络结构以及突触类型。在本研究中，我们考虑了突触相互作用（电突触和化学突触）以及结构连通性对以β节律规则放电的Izhikevich神经元网络模型中同步转变的影响。我们发现了广泛的行为，包括连续性转变、爆发性转变以及缺乏全局秩序。更强的电突触更有利于同步，甚至能导致爆发性同步。我们发现，决定转变顺序的关键网络元素是聚类系数，而非小世界效应或网络中枢纽的存在。这些结果与之前使用诸如Kuramoto模型等相位振荡器模型的结果形成对比。此外，我们表明，当进入γ波段时，同步模式会发生变化。我们将这种变化归因于Izhikevich神经元不应期的变化，其随频率显著变化。
