School of Mathematics and Statistics, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
Center for Mathematical Sciences, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
Biol Cybern. 2022 Dec;116(5-6):545-556. doi: 10.1007/s00422-022-00942-9. Epub 2022 Aug 31.
Neuronal network synchronization has received wide interest. In the present manuscript, we study the influence of initial membrane potentials together with network topology on bursting synchronization, in particular the sequential order of stabilized bursting among neurons. We find a hierarchical phenomenon on their bursting order. With a focus on situations where network coupling advances spiking times of neurons, we grade neurons into different layers. Together with the neuronal network structure, we construct directed graphs to indicate bursting propagation between different layers. More explicitly, neurons in upper layers burst earlier than those in lower layers. More interestingly, we find that among the same layer, bursting order of neurons is mainly associated with the number of neurons they connected to the upper layer; more stimuli lead to earlier bursting. Receiving effectively the same stimuli from the upper layer, we observe neurons with fewer connections would burst earlier.
神经网络同步受到了广泛关注。在本文中,我们研究了初始膜电位以及网络拓扑结构对爆发同步的影响,特别是神经元之间稳定爆发的顺序。我们发现了它们爆发顺序的分层现象。我们关注的是网络耦合会提前神经元尖峰时间的情况,将神经元分为不同的层。结合神经元网络结构,我们构建有向图来表示不同层之间的爆发传播。更明确地说,上层神经元比下层神经元更早爆发。更有趣的是,我们发现同一层中,神经元的爆发顺序主要与它们与上层连接的神经元数量有关;受到更多刺激会导致更早的爆发。在上层接收到相同的有效刺激时,我们观察到连接较少的神经元会更早爆发。