Bio-Inspired Models of Network, Information, and Computing Systems. 5th International ICST Conference, BIONETICS 2010, Boston, USA, December 1-3, 2010, Revised Selected Papers

Research Article

Firing Pattern of Default Mode Brain Network with Spiking Neuron Model

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  • @INPROCEEDINGS{10.1007/978-3-642-32615-8_62,
        author={Teruya Yamanishi and Jian-Qin Liu and Haruhiko Nishimura},
        title={Firing Pattern of Default Mode Brain Network with Spiking Neuron Model},
        proceedings={Bio-Inspired Models of Network, Information, and Computing Systems. 5th International ICST Conference, BIONETICS 2010, Boston, USA, December 1-3, 2010, Revised Selected Papers},
        proceedings_a={BIONETICS},
        year={2012},
        month={10},
        keywords={spiking neuron model default mode brain network spontaneous activity firing rate synchronization},
        doi={10.1007/978-3-642-32615-8_62}
    }
    
  • Teruya Yamanishi
    Jian-Qin Liu
    Haruhiko Nishimura
    Year: 2012
    Firing Pattern of Default Mode Brain Network with Spiking Neuron Model
    BIONETICS
    Springer
    DOI: 10.1007/978-3-642-32615-8_62
Teruya Yamanishi1,*, Jian-Qin Liu2, Haruhiko Nishimura3
  • 1: Fukui University of Technology
  • 2: National Institute of Information and Communications Technology
  • 3: University of Hyogo
*Contact email: yamanisi@fukui-ut.ac.jp

Abstract

Recently, analyses of fMRI data have revealed functionally connected and interacting spontaneous active regions in the brain, which are referred as ”Default Mode Brain Network”. The fluctuations on BOLD signals of the default mode brain network have shown spatiotemporally correlated synchronization at a rate lower than 0.1 Hz in contrast to signals under concrete tasks like high frequency rhythms. Here we construct the default mode brain network by functionally connecting a neural network using functional correlation factors. For numerical simulations with Izhikevich’s spiking neuron model, the condition on the slow synchronization of this network model is fixed, and the network dynamics is analyzed.