Detection and control of epileptiform regime in the Hodgkin-Huxley artificial neural networks via quantum algorithms


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Borisenok S.

The Tenth International Scientific Conference on Physics and Control PhysCon2021, Shanghai, China, 4 - 08 October 2021, pp.32

  • Publication Type: Conference Paper / Summary Text
  • City: Shanghai
  • Country: China
  • Page Numbers: pp.32

Abstract

The Hodgkin Huxley (HH) elements connected to an artificial neural network (ANN) demonstrate variety of their behavior such as resting, singular spikes and spike trains and bursts. This dynamical richness can cause an epileptiform regime originated in the hyper synchronization of the neuron outcomes. Our model covers the detection and suppression of pre-ictal and ictal behavior in a small population of HH cells. The model fol lows our general approach (Borisenok, Çatmabacak, Ünal, 2018) for neuron driving the collective neural bursting, but here we use a quantum paradigm based algorithm emulated with the pair of HH neurons. Such emulation becomes possible due to the complexity of the individual 4 D HH dynamics (Borisenok, 2021). The linear chain of two HH neurons is connected to the rest of ANN and works autonomously. The first neuron plays a role of the detecting element for the hyper synchronization in the ANN and the quantum a lgorithm emulator; while the second one works as a measuring element (emulation of the quantum measurement converting the signals into the classical domain) and the trigger for the feedback suppressing the epileptiform regime. We cover few alternative approaches for the detecting suppressing HH pair (gradient feedback, target attractor feedback), and discuss their pros and cons to compare with our classical model of the epileptiform suppression.