Coexisting firing patterns and phase synchronization in locally active memristor coupled neurons with HR and FN models

Li, Zhijun; Zhou, Hongfu; Wang, Mengjiao; Ma, Minglin

doi:10.1007/s11071-021-06315-4

Cited by 116 publications

(37 citation statements)

References 60 publications

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“…Furthermore, the stable regions of the memristor are (−∞, −2), (−2, 0), (0, 2), and (2, +∞). The local activity of memristors can be validated by the DC V − I plot [7]. Figure 2b depicts the DC V − I plot of the proposed memristor, and the partial enlargement of the curves is shown on the lower right, where the curves with negative slopes are locally active regions of the memristor.…”

Section: Four‐stable Memristor Modelmentioning

confidence: 99%

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Regulation of firing rhythms in a four‐stable memristor‐based Hindmarsh–Rose neuron

Zhou

2021

Electronics Letters

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This letter presents a novel locally active memristor with four-stable pinched hysteresis loops which has never been reported before. The non-volatile memory and local activity of the memristor are verified by the power-off plot and DC V-I plot. A memristive Hindmarsh-Rose (HR) neuron model is constructed by employing the proposed memristor to characterize the electromagnetic induction effect on the membrane potential. Co-existing multiple firing patterns are explored under different memristor initial states. Interestingly, the firing rhythms of the neuron model can be regulated by adjusting the memristor initial values in different stable regions. Circuit simulations are included to verify the theoretical analysis.

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Section: Four‐stable Memristor Modelmentioning

confidence: 99%

“…As we know, the power‐off plot (POP) is utilized to illustrate the non‐volatile memory of memristors [7]. The POP of the four‐stable memristor is displayed in Figure 2a, from which seven equilibrium points can be observed.…”

Section: Four‐stable Memristor Modelmentioning

confidence: 99%

Regulation of firing rhythms in a four‐stable memristor‐based Hindmarsh–Rose neuron

Zhou

2021

Electronics Letters

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“…In recent years, various constructions of chaotic systems have been proposed [13][14][15][16][17][18]. So far, chaotic systems have been widely used in various fields that already exist, such as synchronization [19][20][21], secure communication [22,23], neural network chaotic systems [24][25][26][27][28][29][30][31][32], and PRNGs [33][34][35][36][37][38]. Among them, PRNGs based on chaotic systems are the most fundamental applications of chaotic systems.…”

Section: Introductionmentioning

confidence: 99%

Design and FPGA Implementation of a Pseudo-random Number Generator Based on a Hopfield Neural Network Under Electromagnetic Radiation

Zhang

Shen

et al. 2021

Front. Phys.

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When implementing a pseudo-random number generator (PRNG) for neural network chaos-based systems on FPGAs, chaotic degradation caused by numerical accuracy constraints can have a dramatic impact on the performance of the PRNG. To suppress this degradation, a PRNG with a feedback controller based on a Hopfield neural network chaotic oscillator is proposed, in which a neuron is exposed to electromagnetic radiation. We choose the magnetic flux across the cell membrane of the neuron as a feedback condition of the feedback controller to disturb other neurons, thus avoiding periodicity. The proposed PRNG is modeled and simulated on Vivado 2018.3 software and implemented and synthesized by the FPGA device ZYNQ-XC7Z020 on Xilinx using Verilog HDL code. As the basic entropy source, the Hopfield neural network with one neuron exposed to electromagnetic radiation has been implemented on the FPGA using the high precision 32-bit Runge Kutta fourth-order method (RK4) algorithm from the IEEE 754-1985 floating point standard. The post-processing module consists of 32 registers and 15 XOR comparators. The binary data generated by the scheme was tested and analyzed using the NIST 800.22 statistical test suite. The results show that it has high security and randomness. Finally, an image encryption and decryption system based on PRNG is designed and implemented on FPGA. The feasibility of the system is proved by simulation and security analysis.

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“…These coupled neurons are built with either identical or nonidentical neurons described by the same physical equation. In contrast, previous research has rarely studied the dynamic behavior of coupled neurons, as described by different mathematical models [44]. Therefore, more diverse neural network models, as well as memristive neural networks, must be constructed based on neurons with different mathematical models [45].…”

Section: Introductionmentioning

confidence: 99%

Hamiltonian energy computation and complex behavior of a small heterogeneous network of three neurons: circuit implementation

et al. 2021

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Brain functions are sometimes emulated using some analog integrated circuits based on the organizational principle of natural neural networks. Neuromorphic engineering is the research branch devoted to the study and realization of such circuits with striking features. In this contribution, a novel small network of three neurons is introduced and investigated. The model is built from the coupling between two 2D Hindmarsh–Rose neurons through a 2D FitzHugh–Nagumo neuron. Thus, a heterogeneous coupled network is obtained. The biophysical energy released by the network during each electrical activity is evaluated. In addition, nonlinear analysis tools such as two-parameter Lyapunov exponent, bifurcation diagrams, the graph of the largest Lyapunov exponent, phase portraits, time series, as well as the basin of attractions are used to numerically investigate the network. It is found that the model can experience hysteresis justified by the simultaneous existence of three distinct electrical activities using the same set of parameters. Finally, the circuit implementation of the network is addressed in PSPICE to further support the obtained results.

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Coexisting firing patterns and phase synchronization in locally active memristor coupled neurons with HR and FN models

Cited by 116 publications

References 60 publications

Regulation of firing rhythms in a four‐stable memristor‐based Hindmarsh–Rose neuron

Regulation of firing rhythms in a four‐stable memristor‐based Hindmarsh–Rose neuron

Design and FPGA Implementation of a Pseudo-random Number Generator Based on a Hopfield Neural Network Under Electromagnetic Radiation

Hamiltonian energy computation and complex behavior of a small heterogeneous network of three neurons: circuit implementation

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