Dynamics explore of an improved HR neuron model under electromagnetic radiation and its applications

An, Xinlei; Shi, Qianqian; Qiao, Shuai; Zhang, Li

doi:10.1007/s11071-023-08320-1

Cited by 32 publications

(4 citation statements)

References 40 publications

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“…[25] A Hindmarsh-Rose neuron model is improved by introducing magnetic flux and electric field variables, and the model has abundant dynamics and is applied in image encryption. [26] Neuronal models with electromagnetic induction play a significant role in exploring nonlinear dynamics and biophysical mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Dynamics and synchronization of neural models with memristive membranes under energy coupling

Wan 万,

Wu 吴,

Ma 马

et al. 2024

Chinese Phys. B

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Dynamical modeling of neural systems plays an important role in explaining and predicting some features of biophysical mechanisms. The electrophysiological environment inside and outside of the nerve cell is different. Given the continuous and periodical properties of electromagnetic fields in the cell during its working, electronic components involving two capacitors and a memristor are effective in mimicking the physical features. In this paper, a neural circuit is reconstructed by two capacitors connected by a memristor with periodical mem-conductance. It is found that the memristive neural circuit can present abundant firing patterns without stimulus. The Hamilton energy function is deduced by using the Helmholtz theorem. Further, the neuronal network consisting of memristive neurons is proposed by introducing energy coupling. The controllability and flexibility of parameters make the model have the ability to describe the dynamics and synchronization behaviors.

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Section: Introductionmentioning

confidence: 99%

Dynamics and synchronization of neural models with memristive membranes under energy coupling

Wan 万,

Wu 吴,

Ma 马

et al. 2024

Chinese Phys. B

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“…Memristors are the fourth basic circuit component with natural memory function, and their appearance supplements the relationship between basic variable charges and magnetic flux in circuit theory [1]. Since a physical model of memristor was developed by Hewlett-Packard [2] in 2008, it has been widely introduced into chaotic systems due to the inherent nonlinearity of memristors [3][4][5][6][7][8][9][10][11][12][13]. Numerous studies have shown that chaotic systems based on memristors exhibit rich dynamic behaviors.…”

Section: Introductionmentioning

confidence: 99%

Analysis of a time-dependent memristor-based chaotic system and its application in image encryption

Xiong,

Wang,

2024

Phys. Scr.

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Compared with ordinary chaotic systems, memristor-based chaotic systems have more complex dynamic behaviors and are more suitable for image encryption algorithms. In this paper, a four-dimensional chaotic system is constructed by introducing a cubic nonlinear memristor into a three-dimensional chaotic system. Firstly, the dynamic characteristics of the constructed memristor-based chaotic system are analyzed in detail, and the simulation results show that the system has different attractors with different topological structures at different simulation times. Within a fixed simulation time, the system has 15 attractors with different topological structures under different parameter values, and there is a phenomenon of multiple stability in the system, indicating high complexity. Based on the above discoveries, a color image encryption algorithm including scrambling and diffusion is designed. Experimental results show that this algorithm can perfectly hide the information of the plaintext image, and the decrypted image is consistent with the plaintext image. Finally, the security of the algorithm is analyzed by using key space and so on. The analysis results indicate that the encryption algorithm designed in this paper can effectively resist external attacks and has high security.

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“…Tan et al [26] coupled two 2D HR neurons with a locally active nonvolatile memristor and found that complex firing activity occurred only within the locally active region of the memristor. On the other hand, in complex and variable electrophysiological environments, fluctuations in neuronal membrane potentials can generate a changing magnetic field, which in turn affects neuronal membrane potentials, this effect is known as electromagnetic radiation [27][28][29][30]. Electromagnetic radiation effect can be well modeled by a flux-controlled memristor.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we consider that the firing activities of the HR neuron are affected by multiple factors, including the feedback current from a memristive autapse and the interference from external electromagnetic radiation. Based on the previous studies which considered nonvolatile memristors as autapses in HR and FHN neurons [27], we propose a switchable memristor to simulate autapse, it can switch between volatility and non-volatility, thereby better simulating synaptic plasticity. There is evidence that the updating mechanism of synaptic weights and the synaptic homeostatic plasticity plays an important role in brain memory storage [35].…”

Section: Introductionmentioning

confidence: 99%

Switchable memristor-based Hindmarsh-Rose neuron under electromagnetic radiation

Zhang,

2024

Nonlinear Dyn

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Memristors are prevalently used to simulate biological neuronal synapses due to their unique memductance plasticity and memory effects. A new switchable memristor, which can be configured as a nonvolatile discrete memristor, a nonvolatile continuum memristor or a volatile memristor by adjusting its internal parameter, is proposed to mimic the autapse of the Hindmarsh-Rose (HR) neuron. In the meantime, a flux-controlled memristor is introduced to simulate the effect of external electromagnetic radiation on the HR neuron, thus, an improved 4D HR neuron model without equilibrium points is developed in this study. The hidden firing activities related to the strength of autapse and the electromagnetic radiation intensity are revealed through phase diagrams, time series, bifurcation diagrams, Lyapunov exponent spectrums, and two-parameter dynamical maps. More interestingly, it is found that the memory attributes of memristive autapse play an important role in the firing activities of the neuron, which can induce the mutual transition among periodic spiking with different frequencies and chaotic firing. Additionally, the transition between periodic and chaotic firing induced by the initial value of the switchable memristor is also discovered when it is configured as three different types of memristors. Finally, a neuron circuit is designed with the current-mode devices to improve accuracy and reduce power consumption. The Multisim simulation results are provided to validate the correctness of the neuron model and the effectiveness of numerical analysis.

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Dynamics explore of an improved HR neuron model under electromagnetic radiation and its applications

Cited by 32 publications

References 40 publications

Dynamics and synchronization of neural models with memristive membranes under energy coupling

Dynamics and synchronization of neural models with memristive membranes under energy coupling

Analysis of a time-dependent memristor-based chaotic system and its application in image encryption

Switchable memristor-based Hindmarsh-Rose neuron under electromagnetic radiation

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