7th International Conference on Engineering and Natural Sciences (ICENS 2021), Sarajevo, Bosnia And Herzegovina, 23 - 27 June 2021, pp.60-66
The model for controlling epilepsy discussed here is based on the seizures suppression experimental methods via the electrical stimulation of brain. It has a potential of “fine tuning” according to the epileptic pathology specifics of patients. We consider here a simplified case of an artificial neural network (ANN) with the Hodgkin-Huxley elements providing the necessary variety of dynamical regimes: individual neuron spikes and bursts which could cause the hyper-synchronized behavior of epileptiform type in the whole network. We perform a fine control of the ANN dynamics with control elements which play two roles: they detect the coming seize and send a feedback signal to other neurons to suppress the epileptiform dynamics. To increase the quality and efficiency of the control we study non-classical (based on the quantum paradigm) algorithm. Recently we demonstrated the ability of a pair of Hodgkin-Huxley neurons to emulate some quantum classification and searching algorithms in a relatively profitable way. Here we develop our approach to detect and suppress epileptiform dynamics in the small ANN. We study the efficiency and robustness of our proposed algorithm and discuss its pros and cons to compare with our recent classical algorithm-based model of the epileptiform suppression.