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Discrete memristive neuron model and its interspike interval-encoded application in image encryption

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Abstract

Bursting is a diverse and common phenomenon in neuronal activation patterns and it indicates that fast action voltage spiking periods are followed by resting periods. The interspike interval (ISI) is the time between successive action voltage spikes of neuron and it is a key indicator used to characterize the bursting. Recently, a three-dimensional memristive Hindmarsh-Rose (mHR) neuron model was constructed to generate hidden chaotic bursting. However, the properties of the discrete mHR neuron model have not been investigated, yet. In this article, we first construct a discrete mHR neuron model and then acquire different hidden chaotic bursting sequences under four typical sets of parameters. To make these sequences more suitable for the application, we further encode these hidden chaotic sequences using their ISIs and the performance comparative results show that the ISI-encoded chaotic sequences have much more complex chaos properties than the original sequences. In addition, we apply these ISI-encoded chaotic sequences to the application of image encryption. The image encryption scheme has a symmetric key structure and contains plain-text permutation and bidirectional diffusion processes. Experimental results and security analyses prove that it has excellent robustness against various possible attacks.

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Correspondence to Han Bao.

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This work was supported by the National Natural Science Foundation of China (Grant Nos. 51777016, 51607013 and 62071142).

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Bao, H., Hua, Z., Liu, W. et al. Discrete memristive neuron model and its interspike interval-encoded application in image encryption. Sci. China Technol. Sci. 64, 2281–2291 (2021). https://doi.org/10.1007/s11431-021-1845-x

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