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Bifurcation and oscillation in a time-delay neural mass model

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Abstract

The neural mass model developed by Lopes da Silva et al. simulates complex dynamics between cortical areas and is able to describe a limit cycle behavior for alpha rhythms in electroencephalography (EEG). In this work, we propose a modified neural mass model that incorporates a time delay. This time-delay model can be used to simulate several different types of EEG activity including alpha wave, interictal EEG, and ictal EEG. We present a detailed description of the model’s behavior with bifurcation diagrams. Through simulation and an analysis of the influence of the time delay on the model’s oscillatory behavior, we demonstrate that a time delay in neuronal signal transmission could cause seizure-like activity in the brain. Further study of the bifurcations in this new neural mass model could provide a theoretical reference for the understanding of the neurodynamics in epileptic seizures.

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Acknowledgments

This work was supported by the Key Program of Natural Science Foundation of Shandong Province (No. ZR2013FZ002), the Program of Science and Technology of Suzhou under Grant ZXY2013030, and the Independent Innovation Foundation of Shandong University under Grant 2012DX008.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong University, 27 Shanda Road, Jinan, 250100, People’s Republic of China

    Shujuan Geng, Weidong Zhou, Qi Yuan & Zhen Ma

  2. School of Information and Electric Engineering, Shandong Jianzhu University, Jinan, 250100, People’s Republic of China

    Shujuan Geng

  3. Suzhou Institute, Shandong University, Suzhou, 215123, People’s Republic of China

    Shujuan Geng, Weidong Zhou, Xiuhe Zhao, Qi Yuan, Zhen Ma & Jiwen Wang

  4. Qilu Hospital, Shandong University, Jinan, 250100, People’s Republic of China

    Xiuhe Zhao & Jiwen Wang

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  1. Shujuan Geng
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Correspondence to Weidong Zhou.

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Geng, S., Zhou, W., Zhao, X. et al. Bifurcation and oscillation in a time-delay neural mass model. Biol Cybern 108, 747–756 (2014). https://doi.org/10.1007/s00422-014-0616-4

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