Feedback Control-Based Parallel Memristor-Coupled Sine Map and its Hardware Implementation

Wang, Mengjiao; An, Mingyu; Zhang, Xinan; Iu, Herbert Ho Ching

doi:10.1109/tcsii.2023.3293109

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…[24][25][26][27] Due to the non-volatile, nanoscale, memory properties of memristors, and the similarity between nano-scale moving particles in memristors and mobile neu-rotransmitters in biological synapses, memristors are often considered as ideal candidates for simulating synapses. [28][29][30][31][32] For example, Bao et al [33] established a discrete neuron network containing two identical Rulkov neurons, and regarded the current flowing through the memristor as the electromagnetic induction current to analyze the effect of electromagnetic induction on the dynamic behavior of neuron network. Under the influence of the electromagnetic induction current, the model can achieve complete synchronization and lag synchronization.…”

Section: Introductionmentioning

confidence: 99%

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Yan 晏,

Li 李,

Li 李

2024

Chinese Phys. B

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Research on discrete memristor-based neural networks has received much attention. However, current research mainly focuses on memristor–based discrete homogeneous neuron networks, while memristor-coupled discrete heterogeneous neuron networks are rarely reported. In this study, a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V-I diagram. Based on two-dimensional discrete Izhikevich neuron and two-dimensional discrete Chialvo neuron, a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous neurons. Considering the coupling strength as the control parameter, chaotic firing, periodic firing and hyperchaotic firing patterns are revealed. In particular, multiple coexisting firing patterns are observed, which are induced by different initial values of the memristor. Phase synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling strength. Furthermore, the effect of Gaussian white noise on synchronization behaviors is also explored. We demonstrate that the presence of noise not only leads to the transition of firing patterns, but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.

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Section: Introductionmentioning

confidence: 99%

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Yan 晏,

Li 李,

Li 李

2024

Chinese Phys. B

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“…A memristor [1][2][3][4][5], as the fourth basic electronic component, describes the relationship between charge and magnetic flux. Due to its nanoscale, memorability, nonlinearity, and excellent bionic properties, a large number of researchers have introduced it into neural networks as a synapse [6][7][8][9] or an autapse [10], generating rich dynamic behaviors, such as coexistence [11,12] and multistability [13,14].…”

Section: Introductionmentioning

confidence: 99%

Phase Synchronization and Dynamic Behavior of a Novel Small Heterogeneous Coupled Network

Wang,

Peng,

et al. 2023

Fractal Fract

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Studying the firing dynamics and phase synchronization behavior of heterogeneous coupled networks helps us understand the mechanism of human brain activity. In this study, we propose a novel small heterogeneous coupled network in which the 2D Hopfield neural network (HNN) and the 2D Hindmarsh–Rose (HR) neuron are coupled through a locally active memristor. The simulation results show that the network exhibits complex dynamic behavior and is different from the usual phase synchronization. More specifically, the membrane potential of the 2D HR neuron exhibits five stable firing modes as the coupling parameter k1 changes. In addition, it is found that in the local region of k1, the number of spikes in bursting firing increases with the increase in k1. More interestingly, the network gradually changes from synchronous to asynchronous during the increase in the coupling parameter k1 but suddenly becomes synchronous around the coupling parameter k1 = 1.96. As far as we know, this abnormal synchronization behavior is different from the existing findings. This research is inspired by the fact that the episodic synchronous abnormal firing of excitatory neurons in the hippocampus of the brain can lead to diseases such as epilepsy. This helps us further understand the mechanism of brain activity and build bionic systems. Finally, we design the simulation circuit of the network and implement it on an STM32 microcontroller.

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Design, Hardware Implementation, and Application in Video Encryption of the 2-D Memristive Cubic Map

Gao,

Iu,

Wang

et al. 2024

IEEE Internet Things J.

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Feedback Control-Based Parallel Memristor-Coupled Sine Map and its Hardware Implementation

Cited by 3 publications

References 18 publications

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Phase Synchronization and Dynamic Behavior of a Novel Small Heterogeneous Coupled Network

Design, Hardware Implementation, and Application in Video Encryption of the 2-D Memristive Cubic Map

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