5 TH INTERNATIONAL CONFERENCE ON ENGINEERING TECHNOLOGY AND INNOVATION, İstanbul, Turkey, 13 - 17 October 2021, pp.26-33
The artificial neural network (ANN) of the Hodgkin-Huxley (HH) neurons demonstrates the variety of regimes for collective spiking and bursting. Particularly, it can cause an epileptiform behavior originated in the hyper-synchronization of the neuron outcomes.In (Borisenok, Catmabacak, Unal, 2018) we proposed the classical model for driving the collective HH neural bursting. Here we discuss the new type of more effective and robust quantum paradigm-based algorithm for detecting and suppressing the epileptiform regime in the small population of Hodgkin-Huxley neurons. Another novelty is the absence of specially designed control elements imbedded to the ANN for the ictal phase detection. The distributed scheme of feedback applies the control signals to the regular HH neurons and makes them to monitor the collective dynamics and drive themselves out of the epileptiform phase. We cover few alternative approaches for the feedback (gradient feedback, target attractor feedback), and discuss their pros and cons to compare with our classical model of the epileptiform suppression.