@article{e55741148fed496189cee068c4a1161f,
title = "Macroscopic cluster organizations change the complexity of neural activity",
abstract = "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.",
keywords = "Complex network theory, Complexity, Computational model, Network structure, Self-organization, Spiking neuron",
author = "Jihoon Park and Koki Ichinose and Yuji Kawai and Junichi Suzuki and Minoru Asada and Hiroki Mori",
note = "Funding Information: This research and development work was supported by the Ministry of Internal Affairs and Communications, Japan Society for the Promotion of Science Grand-in-Aid for Scientific Research Grant No. 24119002, The Ministry of Education, Culture, Sports, Science and Technology Grant No. 24680024, New Energy And Industrial Technology Development Organization (Project for Innovative AI Chips and Next-Generation Computing Technology Development/(2) Development of next-generation computing technologies/Exploration of Neuromorphic Dynamics towards Future Symbiotic Society), and JST CREST Grant No. JPMJCR17A4, Japan. Publisher Copyright: {\textcopyright} 2019 by the authors.",
year = "2019",
month = feb,
day = "1",
doi = "10.3390/e21020214",
language = "English",
volume = "21",
journal = "Entropy",
issn = "1099-4300",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "2",
}