Fractional-Order Heterogeneous Neuron Network With Hr Neuron and Fhn Neuron Based on Coupled Locally-Active Memristors: Super Coexisting Firing Behaviors, Bursting Behaviors and its Application

Ding, Dawei; Xiao, Heng; Yang, Zongli; Luo, Honglin; Hu, Yongbing; Liu, Yan; Wang, Mouyuan

doi:10.2139/ssrn.4133023

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…In 2008, HP laboratory successfully manufactured the solid memristor by nanotechnology, [35] which physically proved the existence of memristor. Memristor has the characteristics of nonlinearity and memory function, and it is widely used in nanotechnology, [36,37] neural network, [38][39][40][41] circuit design, [42][43][44][45] secure communication, [46,47] and other fields. Researchers applied memristors to simulate biological synapses based on the remarkable bionic characteristics of memristors, such as memorability and nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor

XiaoHua

Yang

et al. 2023

Chinese Phys. B

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At present, many neuron models have been proposed, which can be divided into discrete neuron models and continuous neuron models. Discrete neuron models have the advantage of faster simulation speed and the ease of understanding complex dynamic phenomena. Due to the properties of memorability, nonvolatility and local activity, locally active discrete memristors (LADMs) are also suitable for simulating synapses. In this paper, we use a LADM to mimic synapses and establish a Rulkov neural network model. It is found that the change of coupling strength and the initial state of the LADM leads to multiple firing patterns of the neural network. In addition, considering the influence of neural network parameters and the initial state of the LADM, numerical analysis methods such as phase diagram and timing diagram are used to study the phase synchronization. As the system parameters and the initial states of the LADM change, the LADM coupled Rulkov neural network exhibits synchronization transition and synchronization coexistence.

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Section: Introductionmentioning

confidence: 99%

Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor

XiaoHua

Yang

et al. 2023

Chinese Phys. B

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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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Dynamic Behavior Analysis and Synchronization of Memristor-Coupled Heterogeneous Discrete Neural Networks

Xiong

et al. 2023

Mathematics

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Continuous memristors have been widely studied in recent years; however, there are few studies on discrete memristors in the field of neural networks. In this paper, a four-stable locally active discrete memristor (LADM) is proposed as a synapse, which is used to connect a two-dimensional Chialvo neuron and a three-dimensional KTZ neuron, and construct a simple heterogeneous discrete neural network (HDNN). Through a bifurcation diagram and Lyapunov exponents diagram, the period and chaotic regions of the discrete neural network model are shown. Through numerical analysis, it was found that the chaotic region and periodic region of the neural network based on DLAM are significantly improved. In addition, coexisting chaos and chaos attractors, coexisting periodic and chaotic attractors, and coexisting periodic and periodic attractors will appear when the initial value of the LADM is changed. Coupled by a LADM synapse, two heterogeneous discrete neurons are gradually synchronized by changing the coupling strength. This paper lays a good foundation for the future analysis of LADMs and the related research of discrete neural networks coupled by LADMs.

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Fractional-Order Heterogeneous Neuron Network With Hr Neuron and Fhn Neuron Based on Coupled Locally-Active Memristors: Super Coexisting Firing Behaviors, Bursting Behaviors and its Application

Cited by 3 publications

References 36 publications

Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor

Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor

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

Dynamic Behavior Analysis and Synchronization of Memristor-Coupled Heterogeneous Discrete Neural Networks

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