4.5 Article

Effects of information transmission delay and channel blocking on synchronization in scale-free Hodgkin-Huxley neuronal networks

期刊

ACTA MECHANICA SINICA
卷 27, 期 6, 页码 1052-1058

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s10409-011-0497-x

关键词

Scale-free neuronal networks; Information transmission delay; Ion channel blocking; Synchronization

资金

  1. National Natural Science Foundation of China [11172017, 10972001]
  2. Fujian Natural Science Foundation of China [2009J05004]
  3. Key Project of Fujian Provincial Universities (Information Technology Research Based on Mathematics)

向作者/读者索取更多资源

In this paper, we investigate the evolution of spatiotemporal patterns and synchronization transitions in dependence on the information transmission delay and ion channel blocking in scale-free neuronal networks. As the underlying model of neuronal dynamics, we use the Hodgkin-Huxley equations incorporating channel blocking and intrinsic noise. It is shown that delays play a significant yet subtle role in shaping the dynamics of neuronal networks. In particular, regions of irregular and regular propagating excitatory fronts related to the synchronization transitions appear intermittently as the delay increases. Moreover, the fraction of working sodium and potassium ion channels can also have a significant impact on the spatiotemporal dynamics of neuronal networks. As the fraction of blocked sodium channels increases, the frequency of excitatory events decreases, which in turn manifests as an increase in the neuronal synchrony that, however, is dysfunctional due to the virtual absence of large-amplitude excitations. Expectedly, we also show that larger coupling strengths improve synchronization irrespective of the information transmission delay and channel blocking. The presented results are also robust against the variation of the network size, thus providing insights that could facilitate understanding of the joint impact of ion channel blocking and information transmission delay on the spatiotemporal dynamics of neuronal networks.

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