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Article
Neurosciences
Shaobo He et al.
Summary: This study proposes a new discrete neuron model derived from the Huber-Braun neuron, which introduces additional slow and subthreshold currents and temperature dependent ion channels to study the firing pattern of neurons. Bifurcation, Lyapunov exponents, and sample entropy algorithm are used to analyze the dynamical behaviors of the model. The spatiotemporal behavior of the discrete neuron model is investigated in both single- and two-layer networks, and the induction of spiral waves in the network is studied.
COGNITIVE NEURODYNAMICS
(2023)
Article
Mathematics
Minglin Ma et al.
Article
Computer Science, Hardware & Architecture
Hairong Lin et al.
Summary: In this article, a novel method for designing multistructure chaotic attractors in memristive neural networks is proposed. By utilizing a multipiecewise memristive synapse control in a Hopfield neural network (HNN), various complex multistructure chaotic attractors can be produced. Theoretical analysis and numerical simulation demonstrate that multiple multistructure chaotic attractors with different topologies can be generated by conducting the memristive synapse-control in different synaptic coupling positions. Meanwhile, the number of structures can be easily controlled with the memristor control parameters. Furthermore, a module-based analog memristive neural network circuit is designed, allowing the arbitrary number of multistructure attractors to be obtained by selecting corresponding control voltages. Finally, a novel image encryption cryptosystem with a permutation-diffusion structure is designed and evaluated, exhibiting its excellent encryption performances, especially the extremely high key sensitivity.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Engineering, Mechanical
Mengjiao Wang et al.
Summary: This article proposes a new two-dimensional hyperchaotic map and studies its lossless regulation of the oscillation amplitude of chaotic sequences. The experimental results show that the map exhibits complex dynamical behaviors and can demonstrate multistability.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Mechanical
Haodong Li et al.
Summary: This paper discretizes continuous memristor to obtain a discrete memristor using the rectangular pulse discrete sampling method. By identifying fingerprint characteristics, it satisfies the definition of a generalized memristor. The resulting DLAM-Log map generates complex dynamics and a modulation scheme is proposed, showing potential significance in chaos-based information engineering.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Jie Jin et al.
Summary: The zeroing neural network (ZNN) is a classical and effective method for solving various time-varying problems, widely applied in scientific and industrial realms. Robustness and convergence are two essential criteria in evaluating the quality of the ZNN model, but the adjustability of its convergence speed has been neglected in prior works. To address this issue, a well-designed activation function (WDAF) is proposed, leading to the development of a robust predefined-time convergence ZNN (RPTCZNN) model with adjustable convergence speed. The model is validated through mathematical analysis and simulation experiments, showcasing its superior convergence and robustness in solving dynamic matrix inversion problems and enabling tracking control of robotic manipulators.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Fang Yuan et al.
Summary: This paper introduces the excellent chaos performance of discrete memristors in constructing discrete systems, and proposes cascade, parallel, and composite operations to enhance the diversity and performance of the systems. The operation process is demonstrated through examples, showing the ability to construct new discrete memristive systems with larger parameter space and chaotic region.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Physics, Multidisciplinary
Yan-Mei Lu et al.
Summary: This study constructed a new fractional-order discrete memristor model with significant nonlinearity for simulating the dynamical behaviors of neurons under electromagnetic radiation. The characteristics of integer-order and fractional-order systems were analyzed, showing that the fractional-order system has richer dynamics.
Article
Neurosciences
Quan Xu et al.
Summary: This article presents a memristor-based neuron model that imitates the electromagnetic induction effects and exhibits various electrical activities. Through kinetic analysis, the electrical activities related to memristor parameters and initial conditions are explored. An analog circuit is implemented using discrete components to verify the numerically simulated electrical activities.
COGNITIVE NEURODYNAMICS
(2022)
Article
Engineering, Electrical & Electronic
Qiang Lai et al.
Summary: This study introduces a memristor into an enhanced 1D map model and proposes a new 2D memristive map with infinite fixed points. By analyzing and conducting experiments, it is found that the addition of a memristor increases the chaotic complexity of the map.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2022)
Article
Engineering, Mechanical
Yuexi Peng et al.
Summary: This paper proposes a modified intelligent optimization algorithm for parameter identification of discrete memristive chaotic maps, which shows the best performance in dealing with complex behaviors and accurately identifying the parameters of the original system.
NONLINEAR DYNAMICS
(2022)
Review
Engineering, Mechanical
Minglin Ma et al.
Summary: This paper introduces a locally active discrete memristor model for the first time and analyzes its dynamical behaviors using various methods. The results show that applying the locally active discrete memristor significantly improves the chaotic properties of the map and demonstrates the existence of attractors.
NONLINEAR DYNAMICS
(2022)
Article
Mathematics, Interdisciplinary Applications
Qiang Lai et al.
Summary: This paper presents a novel neuron model by coupling a memristor to obtain a memristive neuron model. The study shows that memristor can enhance the chaos complexity of the discrete neuron, resulting in hyperchaos. Additionally, a new encryption scheme for image encryption is proposed, which exhibits excellent security characteristics.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Engineering, Electrical & Electronic
Zhijun Li et al.
Summary: In this letter, a memristor-based associative memory circuit considering synaptic crosstalk is proposed to investigate its effect on associative memory. The inhibitory effect of negative crosstalk on associative memory in the initial learning stage and the consolidation influence of positive crosstalk on associative memory when the synaptic weight exceeds its critical value are revealed.
ELECTRONICS LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Yan Liang et al.
Summary: Locally-active memristor (LAM) has potential applications in neuromorphic computing as an artificial neuron. Quantitative theoretical analysis on LAMs shows that their performance is closely associated with three crucial parameters: resistance, differential resistance, and a dynamic parameter. These parameters can be used to derive the small-signal equivalent circuit of LAMs and determine the oscillation frequency range and conditions for LAM-based oscillators.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2022)
Article
Computer Science, Artificial Intelligence
Sichun Du et al.
Summary: This paper designs a Pavlov associative memory circuit based on memristor and successfully simulates the blocking phenomenon. The circuit features a complete neural network circuit and the ability to achieve advanced neural activities.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Engineering, Mechanical
Qiuzhen Wan et al.
Summary: This paper investigates a Hopfield neural network under external electromagnetic radiation and dual bias currents, discussing its basic properties and nonlinear dynamic characteristics. The network shows high sensitivity to system parameters and initial conditions due to the presence of radiation and dual bias currents. The study reveals the existence of hidden attractors such as periodic, quasi-periodic, chaotic, and transient chaotic attractors in the proposed network. Additionally, the network exhibits transient chaos with different chaotic times based on varying neuron membrane magnetic flux, and parallel bifurcation behaviors with changing system parameters are observed.
NONLINEAR DYNAMICS
(2022)
Article
Physics, Multidisciplinary
Lilian Huang et al.
Summary: In this paper, a new discrete memristive chaotic map with infinitely wide parameter range is designed. An improved perturbation method is proposed to estimate the output sequence of the chaotic system. Through numerical simulations and mathematical proofs, it is shown that the map is always in chaotic or hyperchaotic state, and the mechanism of its dynamic behavior and initial-boosting behavior are analyzed.
Article
Physics, Multidisciplinary
Ziwei Liang et al.
Summary: This paper presents a novel discrete memristive chaotic map, which enriches the dynamical behaviors of the original map by introducing new properties such as line fixed points and multistability, while also achieving chaotification. The implementation of the memristive chaotic map circuit on the DSP platform demonstrates its potential in practical applications.
EUROPEAN PHYSICAL JOURNAL PLUS
(2022)
Article
Automation & Control Systems
Hairong Lin et al.
Summary: This article focuses on coupled neural networks with brain-like chaotic dynamics and their application in biomedical image encryption. A memristive-coupled neural network (MCNN) model is proposed and its dynamical behaviors are studied. Numerical results show that the MCNN can generate highly complex hyperchaotic attractors and boost the attractor positions by switching their initial states. A biomedical image encryption scheme is designed and its performance is evaluated.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Mathematics, Applied
Qiuzhen Wan et al.
Summary: This paper investigates the behavior of neural networks in complex electromagnetic environments. By simulating the effects of electromagnetic induction and electromagnetic radiation on neural networks, it is found that the system has an unstable origin equilibrium point and exhibits complex dynamic behaviors from regular motions to chaotic motions and back to regular motions. The experimental results validate the numerical analysis and simulations.
Article
Physics, Multidisciplinary
Shaobo He et al.
Summary: This paper investigates the mathematical models of discrete memristors based on Caputo fractional difference and G-L fractional difference, and shows the hysteresis loop characteristics under different input signals. It analyzes the relationship between hysteresis area and input signal frequency and derivative order, discusses the memory effect, characteristics and physical realization of the discrete memristors, and designs a discrete memristor with short memory effects. Furthermore, discrete memristive systems are constructed by introducing fractional-order and integer-order discrete memristors, and the complexity of the systems is controlled. Finally, FPGA digital circuit implementation demonstrates the potential application value of discrete memristors in engineering applications.
Article
Physics, Multidisciplinary
Chunlai Li et al.
Summary: This paper reports two generalized DM models based on the sampling discretization of the generalized CM model, and constructs DM-L map and DM-HR model. The experimental results indicate that DM-L map has linear fixed points, while DM-HR model can generate hidden periodic and chaotic attractors.
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS
(2022)
Article
Engineering, Mechanical
Zhao Yao et al.
Summary: This paper investigates a fractional-order FithzHugh-Nagumo neuron circuit constructed by fractional-order capacitor and inductor based on Adomain decomposition method. The impacts on firing patterns and Hamilton energy are also discussed with the variation of fractional orders. The simulation results suggest that the fractional-order FHN neural model is more suitable for modeling neural behaviors and has promising applications in constructing artificial neural networks.
NONLINEAR DYNAMICS
(2022)
Article
Mathematics
Ling Zhou et al.
Summary: This paper investigates a simple memristor emulator consisting of a diode bridge and a capacitor, and demonstrates its pinched hysteresis loops and higher operating frequency. Through mathematical modeling, it is found that the system only possesses one unstable equilibrium point, and various bifurcations, periodic and chaotic orbits, and coexisting attractors are depicted.
Article
Computer Science, Artificial Intelligence
Yanmei Lu et al.
Summary: This paper investigates the features of memristive-coupled neural networks in the discrete field, and introduces a discrete memristor with sine-type conductance for the first time. The properties of the proposed memristive-coupled bi-neuron Rulkov map and multi-neuron Rulkov neural network are probed using numerical simulation methods, revealing the effects of parameters and coupling factors on the dynamics of the system.
NETWORK-COMPUTATION IN NEURAL SYSTEMS
(2022)
Article
Engineering, Mechanical
Zhenghui Wen et al.
Summary: This paper investigates the effect of magnetic field coupling between neurons on neuron dynamics and finds that it can change the firing mode of neurons, providing new insights into the mechanism of information interaction between neurons.
NONLINEAR DYNAMICS
(2022)
Article
Engineering, Mechanical
Hui Shen et al.
Summary: This paper proposes a novel memristor that is applied to construct neurons and neural networks. By conducting a series of analysis and experiments, the paper verifies its performance in terms of firing dynamics and compares different firing patterns and coupled neural networks. The research results are of great significance for revealing the mystery of the brain and potential applications in practical projects.
NONLINEAR DYNAMICS
(2022)
Article
Physics, Multidisciplinary
Tao Ma et al.
Summary: This paper investigates the nonlinear characteristics of a new 4D Hopfield neural network model with two nonlinear synaptic weights by introducing memristors in the model. Rich and complex dynamical behaviors of the HNNs were analyzed, including periodic windows, chaos, and coexistence of asymmetric attractors. Hardware circuit implementation on a DSP platform showed good consistent performance with Matlab simulations, providing a theoretical basis for the application of HNNs to human brain dynamics.
EUROPEAN PHYSICAL JOURNAL PLUS
(2022)
Article
Physics, Multidisciplinary
Quan Xu et al.
Summary: This paper introduces a continuous non-autonomous memristive Rulkov model, which reveals extreme multistability by considering the effects of electromagnetic induction and external stimulus. Numerical simulations and hardware experiments are conducted to verify the extreme multistability, broadening the future engineering applications of the Rulkov model.
Article
Engineering, Mechanical
Chengjie Chen et al.
Summary: This paper explores the electromagnetic induction effects on memristive HNN with theoretical analysis and numerical methods, discussing various bifurcation behaviors, chaotic phenomena, and extreme events based on different neuron linkage configurations. Hardware experiments confirm the electromagnetic induction effects, demonstrating the potential benefits for future integrated circuit design of large-scale Hopfield neural networks.
NONLINEAR DYNAMICS
(2021)
Article
Engineering, Mechanical
Zhijun Li et al.
Summary: Local activity is considered as the origin of complexity, with locally active memristor proposed in this study exhibiting multiple firing patterns under different initial conditions. The study also investigates phase synchronization of synapse-coupled neurons. Circuit simulations are included to confirm the effectiveness of numerical simulations.
NONLINEAR DYNAMICS
(2021)
Article
Physics, Multidisciplinary
Yibo Wang et al.
Summary: This paper presents an improved dual-memristor-based FitzHugh-Nagumo (DM-FHN) system, which is derived from FitzHugh-Nagumo (FHN) neuron circuit by substituting the tunnel diode with hyperbolic tangent memductance. The analysis model of the system is established and various dynamic behaviors are studied via phase diagram and bifurcation diagram. The coupling synchronization of two DM-FHN neurons and the synchronous dynamics of coupled DM-FHN systems are also investigated.
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS
(2021)
Article
Engineering, Multidisciplinary
Bao Han et al.
Summary: The phenomenon of bursting in neuronal activation patterns is common and diverse, indicating fast action voltage spiking periods followed by resting periods. The time between successive action voltage spikes, known as the interspike interval (ISI), is a key indicator used to characterize bursting. A three-dimensional memristive Hindmarsh-Rose (mHR) neuron model has been constructed to generate hidden chaotic bursting, but the properties of the discrete mHR neuron model have not been studied. This study presents a discrete mHR neuron model and explores different hidden chaotic bursting sequences under four typical parameter sets. Experimental results on ISI-encoded chaotic sequences show complex chaos properties compared to the original sequences. The ISI-encoded chaotic sequences are then applied to image encryption, demonstrating excellent robustness against various possible attacks based on experimental results and security analyses.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2021)
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NONLINEAR DYNAMICS
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Han Bao et al.
NONLINEAR DYNAMICS
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Huijing Sun et al.
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION
(2016)
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Dongpo Hu et al.
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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2013)
Review
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