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Mathematical model of a neuromorphic network based on memristive elements

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  • Yu. Morozov, Alexander
  • Abgaryan, Karine K.
  • Reviznikov, Dmitry L.

Abstract

The article discusses the modeling of interconnected memory elements within a neuromorphic network. A mathematical model is proposed that describes a hardware analog implementation of a spiking neural network with memristive elements as synaptic weights and a learning mechanism based on the STDP (Spike-Timing Dependent Plasticity) rule. As a memristor model, a model describing a hafnium oxide (HfO2) element is used. A numerical simulation of the operation of one and two interconnected neurons with 64 and 128 synapses has been carried out. The process of training the network to recognize certain templates is demonstrated.

Suggested Citation

  • Yu. Morozov, Alexander & Abgaryan, Karine K. & Reviznikov, Dmitry L., 2021. "Mathematical model of a neuromorphic network based on memristive elements," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:chsofr:v:143:y:2021:i:c:s0960077920309395
    DOI: 10.1016/j.chaos.2020.110548
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    1. Spagnolo, B. & Valenti, D. & Guarcello, C. & Carollo, A. & Persano Adorno, D. & Spezia, S. & Pizzolato, N. & Di Paola, B., 2015. "Noise-induced effects in nonlinear relaxation of condensed matter systems," Chaos, Solitons & Fractals, Elsevier, vol. 81(PB), pages 412-424.
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    Cited by:

    1. Lee, Geun Ho & Kim, Tae-Hyeon & Youn, Sangwook & Park, Jinwoo & Kim, Sungjoon & Kim, Hyungjin, 2023. "Low-fluctuation nonlinear model using incremental step pulse programming with memristive devices," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).

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