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Oscillations and synchrony in a cortical neural network

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Abstract

In this paper, the oscillations and synchronization status of two different network connectivity patterns based on Izhikevich model are studied. One of the connectivity patterns is a randomly connected neuronal network, the other one is a small-world neuronal network. This Izhikevich model is a simple model which can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. Detailed investigations reveal that by varying some key parameters, such as the connection weights of neurons, the external current injection, the noise of intensity and the neuron number, this neuronal network will exhibit various collective behaviors in randomly coupled neuronal network. In addition, we show that by changing the number of nearest neighbor and connection probability in small-world topology can also affect the collective dynamics of neuronal activity. These results may be instructive in understanding the collective dynamics of mammalian cortex.

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Acknowledgments

The work is supported by Science and Technology Support Plan Topics (No. 2011BAH24B12), Key Program of National Natural Science Foundation of China (No. 11232005), Fundamental Research Funds for the Central Universities (No. ZXH2012C004) and Start-up Funds of Civil Aviation University (No. 2012QD09X).

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Authors and Affiliations

  1. Tianjin Key Laboratory for Advanced Signal Processing, College of Electronic Information Engineering, Civil Aviation University, Tianjin, 300300, China

    Jingyi Qu

  2. Institute for Cognitive Neurodynamics, School of Science, East China University of Science and Technology, Meilong Road 130, Shanghai, 200237, China

    Rubin Wang, Chuankui Yan & Ying Du

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  1. Jingyi Qu
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  2. Rubin Wang
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  3. Chuankui Yan
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  4. Ying Du
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Correspondence to Jingyi Qu.

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Qu, J., Wang, R., Yan, C. et al. Oscillations and synchrony in a cortical neural network. Cogn Neurodyn 8, 157–166 (2014). https://doi.org/10.1007/s11571-013-9268-7

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