Privacy Protection of Medical Data Based on Multi-Scroll Memristive Hopfield Neural Network

Yu, Fei; Shen, Hui; Yu, Qiulin; Kong, Xinxin; Sharma, Pradip Kumar; Cai, Shuo

doi:10.1109/tnse.2022.3223930

Cited by 129 publications

(36 citation statements)

References 55 publications

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“…In 1971, Chua first put forward the concept of a memristor as a device to describe the relationship between the charge and the flux [26]. Since HP Laboratory developed the entity of memristor in 2008 [27], continuous memristors have been widely used in 2 of 18 neural networks [28][29][30][31][32][33][34] and neural morphological circuits [35][36][37][38][39]. Ding et al [40] studied the hidden coexisting firing patterns of two heterogeneous fractional-order HR neurons coupled with a memristor and applied them to image encryption.…”

Section: Introductionmentioning

confidence: 99%

Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

et al. 2023

Fractal Fract

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In order to enrich the dynamic behaviors of discrete neuron models and more effectively mimic biological neural networks, this paper proposes a bistable locally active discrete memristor (LADM) model to mimic synapses. We explored the dynamic behaviors of neural networks by introducing the LADM into two identical Rulkov neurons. Based on numerical simulation, the neural network manifested multistability and new firing behaviors under different system parameters and initial values. In addition, the phase synchronization between the neurons was explored. Additionally, it is worth mentioning that the Rulkov neurons showed synchronization transition behavior; that is, anti-phase synchronization changed to in-phase synchronization with the change in the coupling strength. In particular, the anti-phase synchronization of different firing patterns in the neural network was investigated. This can characterize the different firing behaviors of coupled homogeneous neurons in the different functional areas of the brain, which is helpful to understand the formation of functional areas. This paper has a potential research value and lays the foundation for biological neuron experiments and neuron-based engineering applications.

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Section: Introductionmentioning

confidence: 99%

Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

et al. 2023

Fractal Fract

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“…In practical applications, power grids [1,2], communication networks, secure communication [3] and public transportation systems [4,5] often encounter failures and attacks [6][7][8][9]. A failure of a very small fraction of nodes in a network may lead to complete fragmentation of the whole network.…”

Section: Introductionmentioning

confidence: 99%

A new effective metric for dynamical robustness of directed networks

2023

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In this article, dynamical robustness of a directed complex network with additive noise is inverstigated. The failure of a node in the network is modeled by injecting noise into the node. Under the framework of mean-square stochastic stability, a new robustness metric is formulated to characterize the robustness of the network in terms of synchronization to the additive noise. It is found that the node dynamics plays a pivotal role in dynamical robustness of the directed network. Numerical simulations are shown for illustration and verification.

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“…No matter which category they are, all these neuron models can effectively reproduce abundant firing patterns in response to the change of the electrophysiological environment. Recently, the electromagnetic induction effect has immersed great scientists' concern, which can greatly affect the neuron dynamics [15][16][17] and neural network behaviors [18,19].…”

Section: Introductionmentioning

confidence: 99%

Hidden firing patterns and memristor initial condition-offset boosting behavior in a memristive Hindmarsh-Rose neuron model

et al. 2023

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Electromagnetic induction can effectively induce abundant firing patterns in neurons. In modeling a neuron model with the electromagnetic induction effect, an electromagnetic induction current is frequently added to the state equation of membrane potential. To more properly reflect the non-uniform distribution of the ions inside and outside the neuron membrane, an ideal flux-controlled memristor with sinusoidal memductance function and non-linearly modulated input is raised to depict an electromagnetic induction effect on a Hindmarsh–Rose neuron model, and thereby, a three-dimensional (3D) memristive Hindmarsh–Rose (mHR) neuron model is built in this paper. The proposed mHR neuron model possesses no equilibrium point since the involvement of the ideal flux-controlled memristor, which induces the generation of hidden dynamics. Numerical results declare that the mHR neuron model can generate abundant hidden dynamics, i.e., periodic spiking, chaotic spiking, period-doubling bifurcation route, tangent bifurcation, and chaos crisis. These hidden dynamics are much related to the memristor coupling strength and externally applied stimulus. Afterward, the memristor initial condition-offset boosting behavior is revealed. This can trigger the generation of infinite multiple coexisting firing patterns along the memristor variable coordinate. These coexisting firing patterns have identical attractor topology but different locations in the phase plane. Finally, an analog circuit is designed for implementing the mHR neuron model, and PSIM-based circuit simulation is executed. The circuit-simulated results perfectly verify the generation of hidden infinite multiple coexisting initial condition-offset boosting firing patterns in the proposed mHR neuron model.

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Privacy Protection of Medical Data Based on Multi-Scroll Memristive Hopfield Neural Network

Cited by 129 publications

References 55 publications

Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

Multistability and Phase Synchronization of Rulkov Neurons Coupled with a Locally Active Discrete Memristor

A new effective metric for dynamical robustness of directed networks

Hidden firing patterns and memristor initial condition-offset boosting behavior in a memristive Hindmarsh-Rose neuron model

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