Firing mechanism based on single memristive neuron and double memristive coupled neurons

Shen, Hui; Yu, Fei; Wang, Chunhua; Sun, Jingru; Cai, Shuo

doi:10.1007/s11071-022-07812-w

Cited by 55 publications

(15 citation statements)

References 65 publications

(46 reference statements)

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“…Based on the memristive Hopfield neural network, neural bursting and synchronization have been imitated by modeling two neural network models [13]. Moreover, the famous Hodgkin−Huxley neuron model with a memristor [14] and firing mechanism for both single memristive neuron and double memristive coupled neurons [15] have been built. From the aforementioned works, it has been widely recognized that memristors have been successfully employed to configure neurons and synapses in a series of neuromorphic circuits.…”

Section: Open Accessmentioning

confidence: 99%

AC power analysis for second-order memory elements

Liu

Liu²,

Luo³

et al. 2023

Front. Phys.

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As the product of a circuit’s voltage and current, apparent power (S) is of paramount necessity and importance in electrical utilities, electronics, communication, and neural network systems. Based on the existing AC power analysis on the two-terminal passive elements (i.e., R, L, and C), some in-depth research on AC apparent power calculations for second-order memory elements and memristive systems is introduced to help with revealing their complex and unique non-linear phenomena. This paper derives the forms of real power, reactive power, and apparent power for the proposed second-order memory elements (i.e., MR, MC, and ML) and reveals the difference between ideal memory elements and traditional passive ones (i.e., R, C, and L). For all involved memory elements, harmonic values and an extra term occur in the expression of powers to represent their memory characteristics. Especially, the real power is a function of a circuit’s dissipative elements (usually resistances R), but not exactly the memristor (MR). Then, the corresponding curves could be depicted, which demonstrate the differences between R/C/L and MR/MC/ML and verified that harmonic values existed in SMR/SMC/SML, meaning that it would perpetually supply energy when operated with an alternating current.

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Section: Open Accessmentioning

confidence: 99%

AC power analysis for second-order memory elements

Liu

Liu²,

Luo³

et al. 2023

Front. Phys.

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show abstract

“…Because of the properties of memorability, nonvolatility, nanoscale, and local activity, memristors are regarded as the best choice to mimic synapses, which have been proved in continuous neuron models. Additionally, phenomena such as coexisting firing patterns [41][42][43][44] and synchronization [45][46][47] have been found. It was found in [48] that anti-phase synchronization helps to distinguish the different functional areas of the brain.…”

Section: Introductionmentioning

confidence: 99%

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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“…Since then, chaos synchronization control has become a very active research topic [4][5][6][7][8][9][10][11][12][13][14][15][16], for example, projective synchronization control and hybrid function projective synchronization control. Many control methods have been used to synchronize different chaotic systems, such as feedback linearization method [17], optimal control [18], and neural network control [19][20][21][22][23][24]. However, few people study the problem of chaos synchronization on a finite time interval.…”

Section: Introductionmentioning

confidence: 99%

A new adaptive iterative learning control of finite-time hybrid function projective synchronization for unknown time-varying chaotic systems

et al. 2023

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A new adaptive iterative learning control (AILC) scheme is proposed to solve the finite-time hybrid function projective synchronization (HFPS) problem of chaotic systems with unknown periodic time-varying parameters. Fourier series expansion (FSE) is introduced to deal with the problem of uncertain time-varying parameters. The bound of the expanded remaining items is unknown. A typical convergent series is used to deal with the unknown bound in the design process of the controller. The adaptive iterative learning synchronization controller and parameter update laws are designed. Two different chaotic systems are synchronized asymptotically according to different proportional functions on a finite time interval by Lyapunov stability analysis. The simulation example proves the feasibility and effectiveness of the proposed method.

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Firing mechanism based on single memristive neuron and double memristive coupled neurons

Cited by 55 publications

References 65 publications

AC power analysis for second-order memory elements

AC power analysis for second-order memory elements

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

A new adaptive iterative learning control of finite-time hybrid function projective synchronization for unknown time-varying chaotic systems

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