Macroscopic cluster organizations change the complexity of neural activity

Jihoon Park*, Koki Ichinose, Yuji Kawai, Junichi Suzuki, Minoru Asada, Hiroki Mori

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


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.

Original languageEnglish
Article number214
Issue number2
Publication statusPublished - 2019 Feb 1
Externally publishedYes


  • Complex network theory
  • Complexity
  • Computational model
  • Network structure
  • Self-organization
  • Spiking neuron

ASJC Scopus subject areas

  • General Physics and Astronomy


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