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Updated: Apr 12, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
A discrete memristive cyclic Hopfield neural network with multi-cavity-like attractors and application in secure
Gang Yang1, Chunhua Wang2, Quanli Deng1
1College of Computer Sicence and Electronic Engineering, Hunan University, Changsha, 410082, China.
Abstract:
Discrete memristors with synapse-like properties play a significant role in elucidating the complex neurodynamic mechanisms of biological neural networks in the brain. This work presents a discrete memristive cyclic Hopfield neural network (DMCHNN), which integrates a novel discrete cosine memristor model for simulating mutual synaptic connections with a cyclic Hopfield neural network. Numerical analysis indicates that the system can control the chaotic range through amplitude modulation of the coupling strength k and weight w31, generating different numbers of multi-cavity-like hyperchaotic attractors under the regulation of k. By varying the initial value of the memristor, the system exhibits homogeneous and heterogeneous initial offset-boosting behaviors, accompanied by the coexistence of numerous homogeneous and heterogeneous attractors. Entropy performance evaluation further confirms that the system possesses excellent randomness. Furthermore, multi-cavity-like attractors produced by the system are successfully implemented on an FPGA hardware platform. Experimental results of the HE-DCSK communication system constructed based on DMCHNN demonstrate superior anti-noise performance compared to other chaotic maps.
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