Park Jihoon, Ichinose Koki, Kawai Yuji, Suzuki Junichi, Asada Minoru, Mori Hiroki
Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Osaka 565-0871, Japan.
Graduate School of Engineering, Osaka University, Suita, Osaka 565-0871, Japan.
Entropy (Basel). 2019 Feb 23;21(2):214. doi: 10.3390/e21020214.
In this study, simulations are conducted using a network model to examine how the macroscopic network in the brain is related to the complexity of activity for each region. The network model is composed of multiple neuron groups, each of which consists of spiking neurons with different topological properties of a macroscopic network based on the Watts and Strogatz model. The complexity of spontaneous activity is analyzed using multiscale entropy, and the structural properties of the network are analyzed using complex network theory. Experimental results show that a macroscopic structure with high clustering and high degree centrality increases the firing rates of neurons in a neuron group and enhances intraconnections from the excitatory neurons to inhibitory neurons in a neuron group. As a result, the intensity of the specific frequency components of neural activity increases. This decreases the complexity of neural activity. Finally, we discuss the research relevance of the complexity of the brain activity.
在本研究中，使用网络模型进行模拟，以检验大脑中的宏观网络如何与每个区域的活动复杂性相关联。该网络模型由多个神经元组组成，每个神经元组由基于Watts和Strogatz模型的具有宏观网络不同拓扑特性的脉冲神经元组成。使用多尺度熵分析自发活动的复杂性，并使用复杂网络理论分析网络的结构特性。实验结果表明，具有高聚类性和高度中心性的宏观结构会提高神经元组中神经元的放电率，并增强神经元组中从兴奋性神经元到抑制性神经元的内部连接。结果，神经活动的特定频率成分的强度增加。这降低了神经活动的复杂性。最后，我们讨论了大脑活动复杂性的研究相关性。
