Fractional-order heterogeneous memristive Rulkov neuronal network and its medical image watermarking application

doi:10.1088/1674-1056/ad322b

Abstract This article proposes a novel fractional heterogeneous neural network by coupling a Rulkov neuron with a Hopfield neural network (FRHNN), utilizing memristors for emulating neural synapses. The study firstly demonstrates the coexistence of multiple firing patterns through phase diagrams, Lyapunov exponents (LEs), and bifurcation diagrams. Secondly, the parameter related firing behaviors are described through two-parameter bifurcation diagrams. Subsequently, local attraction basins reveal multi-stability phenomena related to initial values. Moreover, the proposed model is implemented on a microcomputer-based ARM platform, and the experimental results correspond to the numerical simulations. Finally, the article explores the application of digital watermarking for medical images, illustrating its features of excellent imperceptibility, extensive key space, and robustness against attacks including noise and cropping.

Keywords: fractional order memristors Rulkov neuron medical image watermarking

Received: 26 December 2023
Revised: 22 February 2024
Accepted manuscript online: 11 March 2024

PACS:	05.45.-a	(Nonlinear dynamics and chaos)
	87.18.Sn	(Neural networks and synaptic communication)
	05.45.Vx	(Communication using chaos)

Fund: This study was funded by the National Natural Science Foundation of China (Grant No. 12302070) and the Ningxia Science and Technology Leading Talent Training Program (Grant No. 2022GKLRLX04).

Corresponding Authors: Hongwei Zhang
E-mail: hwzhang@ahu.edu.cn

Cite this article:

Dawei Ding(丁大为), Yan Niu(牛炎), Hongwei Zhang(张红伟), Zongli Yang(杨宗立), Jin Wang(王金), Wei Wang(王威), and Mouyuan Wang(王谋媛) Fractional-order heterogeneous memristive Rulkov neuronal network and its medical image watermarking application 2024 Chin. Phys. B 33 050503

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Fractional-order heterogeneous memristive Rulkov neuronal network and its medical image watermarking application

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