Department of Mathematics, Institute for Cognitive Neurodynamics, School of Science, School of Information Science and Engineering, East China University of Science and Technology, Meilong 130, Shanghai, 200237 China.
Cogn Neurodyn. 2012 Feb;6(1):21-31. doi: 10.1007/s11571-011-9174-9. Epub 2011 Sep 13.
In this paper, we study the synchronization status of both two gap-junction coupled neurons and neuronal network with two different network connectivity patterns. One of the network connectivity patterns is a ring-like neuronal network, which only considers nearest-neighbor neurons. The other is a grid-like neuronal network, with all nearest neighbor couplings. We show that by varying some key parameters, such as the coupling strength and the external current injection, the neuronal network will exhibit various patterns of firing synchronization. Different types of firing synchronization are diagnosed by means of a mean field potential, a bifurcation diagram, a correlation coefficient and the ISI-distance method. Numerical simulations demonstrate that the synchronization status of multiple neurons is much dependent on the network patters, when the number of neurons is the same. It is also demonstrated that the synchronization status of two coupled neurons is similar with the grid-like neuronal network, but differs radically from that of the ring-like neuronal network. These results may be instructive in understanding synchronization transitions in neuronal systems.
在本文中,我们研究了具有两种不同网络连接模式的两个缝隙连接耦合神经元和神经元网络的同步状态。其中一种网络连接模式是环形神经元网络,它只考虑最近邻神经元。另一种是网格状神经元网络,具有所有最近邻连接。我们表明,通过改变一些关键参数,如耦合强度和外部电流注入,可以使神经元网络表现出各种类型的放电同步。通过平均场势、分岔图、相关系数和ISI 距离方法来诊断不同类型的放电同步。数值模拟表明,当神经元数量相同时,多个神经元的同步状态在很大程度上取决于网络模式。还表明,两个耦合神经元的同步状态与网格状神经元网络相似,但与环形神经元网络有很大不同。这些结果可能有助于理解神经元系统中的同步转换。