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.
Highlights
A macroscopic structure shapes its microscopic activity
To investigate how the structural properties of the fundamental network affect the self-organization of the network and the complexity of neural activity, we considered following features of the network using complex network theory: the graph to be analyzed is composed of 100 nodes, and each node corresponds to a neuron group
The main results are as follows: 1. The analysis of multiscale entropy (MSE) showed that each neuron group had different levels of the complexity of neural activity and the average complexity of all neuron groups decreased if the fundamental network had small pWS (Section 3.1)
Summary
A macroscopic structure shapes its microscopic activity. A good building structure keeps intensity of reinforcing steels for a long time. Complex network theory shows that a macroscopic anatomical brain network, which is constructed based on the anatomical connections between brain regions, has unique structural properties that are similar to a small-world network [3]. The high clustering coefficient and low shortest path length in the small-world network enhance the local and global information transmission from one node to other nodes [4,5,6]. The smallworldness of the brain network may allow for efficient information transmission and processing in local and global brain regions [7] It is unclear how the structural properties of the small-world network affect the macroscopic activity of a brain region, the microscopic activities of individual neurons, and the functional network
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