Abstract
The application of external electromagnetic stimulation to regulate the electrophysiological activities of specific brain regions can provide an ideal control or treatment scheme for some non-organic mental diseases. To further explore the effectiveness of electromagnetic stimulation in the treatment of mental illnesses, the regulatory abilities of external electromagnetic stimulation on the pattern dynamics of Newman–Watts small-world neuronal networks are systematically studied. The main stability function is used to construct four periodic or chaotic synchronous networks. Also, the average discharge frequency and the consistency coefficient are selected to measure the regulatory effects of external electromagnetic stimulation on the network dynamics. Numerical experiments show that electromagnetic stimulation can inhibit the electrophysiological activities of neuronal networks. Periodic electromagnetic stimulation with large amplitude or stochastic electromagnetic stimulation with large deviation has a more significant inhibitory effect on the discharge activities, not only effectively desynchronizing the discharge activities, but also controlling the evolution of spatiotemporal patterns, and even inducing the synchronization transition. Additionally, the number of stimulated neurons in neuronal networks also plays an important role in the evolution of spatiotemporal patterns. This study could provide theoretical guidance for the physiological application of electromagnetic stimulation in the treatment of certain mental diseases.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos. 11672233 and 11972292), 111 Project (No. BP0719007), the foundation of National Key Laboratory of Science and Technology on Aerodynamic Design and Research (No. 614220119040101) and the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University (No. CX201925).
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Qu, L., Du, L., Hu, H. et al. Pattern control of external electromagnetic stimulation to neuronal networks. Nonlinear Dyn 102, 2739–2757 (2020). https://doi.org/10.1007/s11071-020-06076-6
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DOI: https://doi.org/10.1007/s11071-020-06076-6