Modeling fluctuations in default-mode brain network using a spiking neural network.

Teruya Yamanishi, Jian-Qin Liu, Haruhiko Nishimura
Author Information
  1. Teruya Yamanishi: Department of Management Information Science, Fukui University of Technology, Fukui, 910-8505, Japan. yamanisi@fukui-ut.ac.jp

Abstract

Recently, numerous attempts have been made to understand the dynamic behavior of complex brain systems using neural network models. The fluctuations in blood-oxygen-level-dependent (BOLD) brain signals at less than 0.1 Hz have been observed by functional magnetic resonance imaging (fMRI) for subjects in a resting state. This phenomenon is referred to as a "default-mode brain network." In this study, we model the default-mode brain network by functionally connecting neural communities composed of spiking neurons in a complex network. Through computational simulations of the model, including transmission delays and complex connectivity, the network dynamics of the neural system and its behavior are discussed. The results show that the power spectrum of the modeled fluctuations in the neuron firing patterns is consistent with the default-mode brain network's BOLD signals when transmission delays, a characteristic property of the brain, have finite values in a given range.

MeSH Term

Action Potentials
Animals
Brain
Computer Simulation
Humans
Models, Neurological
Nerve Net
Neural Networks, Computer
Neural Pathways
Neurons
Nonlinear Dynamics

Word Cloud

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