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A neurohybrid memristive system for adaptive stimulation of hippocampus

Author

Listed:
  • Gerasimova, S.A.
  • Lebedeva, A.V.
  • Fedulina, A.
  • Koryazhkina, M.
  • Belov, A.I.
  • Mishchenko, M.A.
  • Matveeva, M.
  • Guseinov, D.
  • Mikhaylov, A.N.
  • Kazantsev, V.B.
  • Pisarchik, A.N.

Abstract

We propose a hybrid memristve neuromorphic system for stimulating hippocampus regions bypassing damaged areas. Synaptic plasticity properties of the system allow close-loop adaptive control of neural dynamics. We implement the simplest version of this system which consists of two neuron-like generators coupled by a memristive device, and two fiber-optic channels to transmit signals from the generators directly to living cells to stimulate hippocampus regions. The adaptive stimulation nature of the neural cells is provided by a stochastic response of the self-learning memristive device to the signal of the neuron-like generator. A biological model of impaired functioning of the perforating pathway in the rat hippocampus is implemented by damaging the CA3 region, on the base of the electrophysiological signal changes in normal and pathological conditions. The proposed adaptive stimulation technology demonstrates the possibility of restoring the functionality of the perforating pathway by introducing the neuromorphic system into the hippocampus to replace lost areas.

Suggested Citation

  • Gerasimova, S.A. & Lebedeva, A.V. & Fedulina, A. & Koryazhkina, M. & Belov, A.I. & Mishchenko, M.A. & Matveeva, M. & Guseinov, D. & Mikhaylov, A.N. & Kazantsev, V.B. & Pisarchik, A.N., 2021. "A neurohybrid memristive system for adaptive stimulation of hippocampus," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    • Handle: RePEc:eee:chsofr:v:146:y:2021:i:c:s0960077921001569
    DOI: 10.1016/j.chaos.2021.110804
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    References listed on IDEAS

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    1. repec:abf:journl:v:31:y:2020:i:3:p:24253-24254 is not listed on IDEAS
    2. Gina C. Adam & Ali Khiat & Themis Prodromakis, 2018. "Challenges hindering memristive neuromorphic hardware from going mainstream," Nature Communications, Nature, vol. 9(1), pages 1-4, December.
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    Cited by:

    1. Koryazhkina, M.N. & Lebedeva, A.V. & Pakhomova, D.D. & Antonov, I.N. & Razin, V.V. & Budylina, E.D. & Belov, A.I. & Mikhaylov, A.N. & Konakov, A.A., 2025. "Investigation of in vitro neuronal activity processing using a CMOS-integrated ZrO2(Y)-based memristive crossbar," Chaos, Solitons & Fractals, Elsevier, vol. 191(C).
    2. Ivan Kipelkin & Svetlana Gerasimova & Davud Guseinov & Dmitry Pavlov & Vladislav Vorontsov & Alexey Mikhaylov & Victor Kazantsev, 2023. "Mathematical and Experimental Model of Neuronal Oscillator Based on Memristor-Based Nonlinearity," Mathematics, MDPI, vol. 11(5), pages 1-17, March.
    3. Rao, Feng & Kang, Yun, 2023. "Dynamics of a stochastic prey–predator system with prey refuge, predation fear and its carry-over effects," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

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