Regulating memristive neuronal dynamical properties via excitatory or inhibitory magnetic field coupling

Wen, Zhenghui; Wang, Chunhua; Deng, Quanli; Lin, Hairong

doi:10.1007/s11071-022-07813-9

Cited by 53 publications

(15 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neurons are the fundamental units of structure and function in the nervous system, and they form neural networks through various types of connections. [1] Neural networks play a crucial role in fields such as signal processing, [2,3] associative memory, [4,5] robot control, [6] and image processing. [7][8][9] As is widely known, a memristor is a device that describes the relationship between charge and flux.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…These continuous neuron models have played an important role in understanding the generation and transmission of action potential. [19][20][21][22][23] Compared with continuous neuron models, discrete neuron models are more computationally efficient, especially in the modeling of large-scale neuron networks. The modeling of discrete neurons and the analysis of their firing patterns have become a hot topic in the field of neurodynamics in recent years.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Regulating memristive neuronal dynamical properties via excitatory or inhibitory magnetic field coupling

Cited by 53 publications

References 53 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

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

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

Contact Info

Product

Resources

About