Smooth nonlinear fitting scheme for analog multiplierless implementation of Hindmarsh–Rose neuron model

Cai, Jianming; Bao, Han; Xu, Quan; Hua, Zhongyun; Bao, Bocheng

doi:10.1007/s11071-021-06453-9

Cited by 37 publications

(4 citation statements)

References 41 publications

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“…The complexity of the brain has encouraged scientists to study neuronal dynamics [1]. Thus, in the early 1980s, artificial neuron models and artificial neural network models emerged, including but not limited to the Hodgkin-Huxley neuron model (HH) [2], FitzHugh-Nagumo (FHN) neuron model [3], Morris-Lecar neuron model (ML) [4], Hindmarsh-Rose neuron model (HR) [5], Chay neuron model [6], Hopfield neural network (HNN) [7] and the Cellular neural network (CNN) model [8]. The analysis of the dynamics of these models made it possible to reveal several electrical activities in the brain dynamics such as periodic spiking, periodic bursting, and mode transition [9].…”

Section: Introductionmentioning

confidence: 99%

A memristive autapse-synapse neural network: application to image encryption

Zhang¹,

Jiang²,

Nkapkop³

et al. 2023

Phys. Scr.

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With the advent of the physical memristor, various memristive neural network models have been designed and analyzed to mimic some human brain functions. However, there is a realistic issue because many works reported the coupling of neuron models using either memristive synapse or memristive autapse, whereas in the real brain, a neuron can interact with both another neuron (memristive synapse) and with itself (memristive autapse). Two main ideas are developed in this work. First, we investigate the dynamics of two different neurons coupled via memristive synapse and memristive autapse. The analyses indicate that the global dynamics of this highly relies on the neuron's coupling strength. Second, a cryptographic scheme based on both S-box driven block compressive sensing and the memristive autapse synapse model is proposed. Performance analyses indicate that the coupling strength of the proposed neural network model can be adjusted to increase or decrease the security of medical data.

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Section: Introductionmentioning

confidence: 99%

A memristive autapse-synapse neural network: application to image encryption

Zhang¹,

Jiang²,

Nkapkop³

et al. 2023

Phys. Scr.

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“…At present, a large number of literatures have been published on the neuron and neural network models realized by analog circuits. Cai et al [34] proposed a novel smooth nonlinear fitting scheme to realize HR neuron model, which greatly reduces the experimental cost of analog circuit and is conducive to the hardware implementation of large-scale neural network. Bao et al [35] implemented the hyperbolic-type memristive Hopfield neural network on breadboard and gave the same results as the numerical simulations.…”

Section: Introductionmentioning

confidence: 99%

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

Shen

Wang

et al. 2022

Nonlinear Dyn

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Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh-Nagumo neuron and Hindmarsh-Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects.

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“…In [15], an image encryption method based on a multi-scroll memristive system was designed. The analog implementation of Hindmarsh-Rose neuron model was discussed in [16]. Various dynamics of a fractional-order chaotic oscillator were investigated in [17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Chaos-Based Cryptography Algorithm and Its Performance Analysis

El‐Latif

Janarthanan

Abd-El-Atty³

et al. 2022

Mathematics

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Data security represents an essential task in the present day, in which chaotic models have an excellent role in designing modern cryptosystems. Here, a novel oscillator with chaotic dynamics is presented and its dynamical properties are investigated. Various properties of the oscillator, like equilibria, bifurcations, and Lyapunov exponents (LEs), are discussed. The designed system has a center point equilibrium and an interesting chaotic attractor. The existence of chaotic dynamics is proved by calculating Lyapunov exponents. The region of attraction for the chaotic attractor is investigated by plotting the basin of attraction. The oscillator has a chaotic attractor in which its basin is entangled with the center point. The complexity of the chaotic dynamic and its entangled basin of attraction make it a proper choice for image encryption. Using the effective properties of the chaotic oscillator, a method to construct pseudo-random numbers (PRNGs) is proposed, then utilizing the generated PRNG sequence for designing secure substitution boxes (S-boxes). Finally, a new image cryptosystem is presented using the proposed PRNG mechanism and the suggested S-box approach. The effectiveness of the suggested mechanisms is evaluated using several assessments, in which the outcomes show the characteristics of the presented mechanisms for reliable cryptographic applications.

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Smooth nonlinear fitting scheme for analog multiplierless implementation of Hindmarsh–Rose neuron model

Cited by 37 publications

References 41 publications

A memristive autapse-synapse neural network: application to image encryption

A memristive autapse-synapse neural network: application to image encryption

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

A Novel Chaos-Based Cryptography Algorithm and Its Performance Analysis

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