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Control of Network Bursting in a Model Spiking Network Supplied with Memristor—Implemented Plasticity

Author

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  • Sergey V. Stasenko

    (Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia
    Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia)

  • Alexey N. Mikhaylov

    (Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia
    Laboratory of Memristor Nanoelectronics, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia)

  • Victor B. Kazantsev

    (Research Laboratory Neuroelectronics and Memristive Nanomaterials (NEUROMENA Lab), Institute of Nanotechnologies, Electronics and Electronic Equipment Engineering, Southern Federal University, 347922 Taganrog, Russia
    Laboratory of Advanced Methods for High-Dimensional Data Analysis, Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia)

Abstract

We consider an unstructured neuron network model composed of excitatory and inhibitory neurons. The synaptic connections are supplied with spike timing-dependent plasticity (STDP). We take the STDP model implemented using a memristor. In normal conditions, the network forms so-called bursting discharges typical of unstructured living networks in dissociated neuronal cultures. Incorporating a biologically inspired model, we demonstrate how memristive plasticity emulates spike timing-dependent plasticity, which is crucial for regulating synchronous brain activity. We have found that, when the memristor-based STDP for inhibitory connections is activated, the bursting dynamics are suppressed and the network turns to a random spiking mode. The dependence of bursting properties on the degree of the memristor-based STDP plasticity is analyzed. These findings hold implications for advancing invasive neurointerfaces and for the identification and management of epileptiform activity.

Suggested Citation

  • Sergey V. Stasenko & Alexey N. Mikhaylov & Victor B. Kazantsev, 2023. "Control of Network Bursting in a Model Spiking Network Supplied with Memristor—Implemented Plasticity," Mathematics, MDPI, vol. 11(18), pages 1-14, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3888-:d:1238470
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    References listed on IDEAS

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    1. Sergey V. Stasenko & Victor B. Kazantsev, 2023. "Bursting Dynamics of Spiking Neural Network Induced by Active Extracellular Medium," Mathematics, MDPI, vol. 11(9), pages 1-17, April.
    2. Wen, Zihao & Li, Zhijun & Li, Xiang, 2019. "Bursting oscillations and bifurcation mechanism in memristor-based Shimizu–Morioka system with two time scales," Chaos, Solitons & Fractals, Elsevier, vol. 128(C), pages 58-70.
    3. 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.
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    Cited by:

    1. Bartłomiej Garda & Karol Bednarz, 2024. "Comprehensive Study of SDC Memristors for Resistive RAM Applications," Energies, MDPI, vol. 17(2), pages 1-17, January.

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