A1 Refereed original research article in a scientific journal
Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich Neurons
Authors: da Silva Lima, Gabriel; Rosa Cota, Vinícius; Moreira Bessa, Wallace
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication year: 2025
Journal: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Journal name in source: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume: 33
First page : 868
Last page: 880
ISSN: 1534-4320
eISSN: 1558-0210
DOI: https://doi.org/10.1109/TNSRE.2025.3543756
Web address : https://doi.org/10.1109/tnsre.2025.3543756
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/485075013
Closed-loop electricalstimulation of brain structures is one of the most promising techniques to suppress epileptic seizures in drug-resistant refractory patients who are also ineligible to ablative neurosurgery. In this work, an intelligent controller is presented to block the aberrant activity of a network of Izhikevich neurons of three different types, used here to model the electrical activity of the basolateral amygdala during ictogenesis, i.e. its transition from asynchronous to hypersynchronous state. A Lyapunov-based nonlinear scheme is used as the main framework for the proposed controller. To avoid the issue of accessing each neuron individually, local field potentials are used to gain insight into the overall state of the Izhikevich network. Artificial neural networks are integrated into the control scheme to manage unknown dynamics and disturbances caused by brain electrical activity that are not accounted for in the model. Four different cases of ictogenesis induction were tested. The results show the efficacy of the proposed control strategy to suppress epileptic seizures and suggest its capability to address both patient-specific and patient-to-patient variability.
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