忆阻耦合Fitz Hugh-Nagumo神经元及其图像加密应用研究

Research on Memristive FitzHugh-Nagumo Neuron System and Its Application in Image Encryption

  • 摘要: 近年来,忆阻器突触耦合神经元的研究取得了显著进展,然而,多数模型只进行了突触和神经元两者间的耦合,未将神经元周围带电离子产生的磁场效应结合到耦合当中。本文提出了一个新型忆阻器模型,并结合细胞膜上离子交换产生的电磁效应构建了一种含有反馈项的忆阻器耦合Fitz Hugh-Nagumo神经元混沌模型,并对该模型进行分插图和李亚普诺夫指数等动力学特性的分析。根据实验结果,该忆阻器耦合神经元模型在一定参数下可以产生双涡卷吸引子。然后将所提模型产生的混沌序列和DNA三倍体突变编码结合应用在图像加密算法中。实验结果表明,该图像加密算法的密钥空间为2266,密文的信息熵为7.9998,相邻像素相关性非常接近0,即该加密算法具有较高的安全性。

     

    Abstract: In recent years, significant progress has been made in the research of memristor synaptic-coupled neurons. However, most models only coupling the synapses and neurons and fail to incorporate the magnetic field effects generated by the charged ions around the neurons into the coupling. In this paper, a novel memristor model is presented, and in combination with the electromagnetic effect generated by ion exchange on the cell membrane, a memristor-coupled Fitz Hugh-Nagumo neuron chaotic model containing feedback terms is constructed. Theoretical analysis and numerical simulation show that the memristor-coupled neuron model can generate double scroll attractors under certain parameters. Then, the chaotic sequences generated by the proposed model and the DNA triploid mutation encoding are combined and applied in the image encryption algorithm. The experimental results show that the key space of this image encryption algorithm is 2266, the information entropy of the ciphertext is 7.9998, and the correlation of adjacent pixels is very close to 0, so the encryption algorithm has high security.

     

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