Dynamics analysis and hardware implementation of multi-scroll hyperchaotic hidden attractors based on locally active memristive Hopfield neural network

Tang, Dong; Wang, Chunhua; Lin, Hairong; Yu, Fei

doi:10.1007/s11071-023-09128-9

Cited by 53 publications

(7 citation statements)

References 54 publications

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“…[25][26][27][28][29][30] Compared to conventional electronic synapses, neural networks based on memristive synapses can more effectively mimic the complex firing activity of biological neural networks. For instance, memristor-coupled neural networks can generate grid multiscroll attractors, [31][32][33][34] diverse chaotic attractors that are employed in image encryption. [35] Locally active memristorbased Hopfield neural networks can exhibit more complex dynamical behaviors.…”

Section: Introductionmentioning

confidence: 99%

Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

Ma 马,

Xiong 熊,

Li 李

et al. 2024

Chinese Phys. B

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Synaptic crosstalk is a prevalent phenomenon among neuronal synapses, playing a crucial role in the transmission of neural signals. Therefore, considering synaptic crosstalk behavior and investigating the dynamical behavior of discrete neural networks is highly necessary. In this paper, we propose a heterogeneous discrete neural network (HDNN) consisting of a three-dimensional KTz discrete neuron and a Chialvo discrete neuron. These two neurons are coupled mutually by two discrete memristors and the synaptic crosstalk is considered. The impact of crosstalk strength on the firing behavior of the HDNN is explored through bifurcation diagrams and Lyapunov exponents. It is observed that the HDNN exhibits different coexisting attractors under varying crosstalk strengths. Furthermore, the influence of different crosstalk strengths on the synchronized firing of the HDNN is investigated, revealing a gradual attainment of phase synchronization between the two discrete neurons as the crosstalk strength decreases.

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Section: Introductionmentioning

confidence: 99%

Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

Ma 马,

Xiong 熊,

Li 李

et al. 2024

Chinese Phys. B

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“…[31] Yu et al studied the application of memristor neuronal networks in the field of privacy protection and image encryption in media on the basis of multiscroll memristor-coupled neuronal networks. [32][33][34] Similarly, He et al proposed that discrete memristor neuronal networks could be applied to the field of character recognition [35] and Lai et al constructed several memristive neuron models which improved security performance in the field of image recognition. [36][37][38] In the field of human-computer interaction, Sun et al designed a three-dimensional pleasurearousal-dominance emotional spatial memristor circuit, which provided reference data for human-computer emotional interaction for the study of bionic robots.…”

Section: Introductionmentioning

confidence: 99%

Dynamical behaviors in discrete memristor-coupled small-world neuronal networks

Lu 鲁,

Xie 谢,

Lu 卢

et al. 2024

Chinese Phys. B

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Brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each other. The memory characteristic of memristors makes them suitable for simulating neuronal synapses with plasticity. In this paper, a memristor is used to simulate synapse, and a discrete small-world neuronal network is constructed based on Rulkov neurons and its dynamical behavior is explored. We explore the influence of system parameter on the dynamical behaviors of the discrete small-world network, and the system shows a variety of firing patterns such as spiking firing and triangular bursting firing when the neuronal parameter α is changed. The numerical simulation results based on Matlab show that the network topology can affect the synchronous firing behavior of the neuronal network, and the higher the reconnection probability, the number of nearest neurons, and the more significant the synchronization state of neurons. In addition, by increasing the coupling strength of memristor synapses, synchronization performance is promoted. The results of this paper can boost the research process of complex neuronal networks coupled with memristor synapse and further promote the development of neuroscience.

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“…Due to the complexity, unpredictability, and adaptability of nonlinear systems and networks, their applications and research face significant challenges. But with the continuous development of science and technology, the research and application of nonlinear systems and networks are also deepening in various fields, such as chaotic systems [6][7][8][9][10], chaotic circuits [11][12][13][14], nonlinear devices [15][16][17], neural networks [18][19][20][21][22][23][24], neural circuits [25][26][27][28], memristors [29][30][31], system synchronization and control [32][33][34][35][36], system optimization [37-39], and related application fields [40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Editorial: Advances in nonlinear systems and networks, volume II

Yu,

Iu,

Lin

et al. 2024

Front. Phys.

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Dynamics analysis and hardware implementation of multi-scroll hyperchaotic hidden attractors based on locally active memristive Hopfield neural network

Cited by 53 publications

References 54 publications

Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

Dynamical behaviors in discrete memristor-coupled small-world neuronal networks

Editorial: Advances in nonlinear systems and networks, volume II

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