Abstract
A model of coupled neural masses can generate seizure-like events and dynamics similar to those observed during interictal to ictal transitions and thus can be used for theoretical study of the control of epileptic seizures. In an effort to understand the mechanisms underlying epileptic seizures and how to avoid them, we added a control input to this model. Epileptic seizures are always accompanied by hypersynchronous firing of neurons, so research on synchronization among cortical areas is significant for seizure control. In this study, principal component analysis (PCA) was used to identify synchronization clusters composed of several neural masses. A method for calculating the synchronization cluster strength and participation rate is presented. The synchronization cluster strength can be used to identify synchronization clusters and the participation rate can be employed to identify neural masses that participate in the clusters. Each synchronization cluster is controlled as a whole using a proportional-integral-derivative (PID) controller. We illustrate these points using coupled neural mass models of synchronization to show their responses to increased (between node) coupling with and without control. Experiment results indicated that PID control can effectively regulate synchronization between neural masses and has the potential for seizure prevention.
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This work was supported by the Program of Development of Science and Technology of Shandong under Grant 2010GSF10243 and the Independent Innovation Foundation of Shandong University under Grant 2012DX008.
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Ma, Z., Zhou, W., Geng, S. et al. Synchronization regulation in a model of coupled neural masses. Biol Cybern 107, 131–140 (2013). https://doi.org/10.1007/s00422-012-0541-3
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DOI: https://doi.org/10.1007/s00422-012-0541-3