Memristive Hopfield neural network with multiple controllable nonlinear offset behaviors and its medical encryption application

Abstract

Memristors possess inherent nonlinearity and synaptic attributes, rendering them more suited for integration as synapses within neural networks compared to resistors. Furthermore, their utilization induces significant alterations in the dynamic behavior of neural networks, hence bestowing considerable value in the realm of secure communication applications. In this paper, an improved cosine memristor is proposed, three representative memristor Hopfield neural networks (MHNNs) are constructed using it as synapse. The MHNNs exhibit rich initial offset boost behaviors, we summarize these phenomena into four types based on the offset characteristics. In particular, these offset types can be converted by adjusting internal parameters of the memristor. In addition, the MHNNs are constructed and tested using both software and hardware components, validating the accuracy of theoretical simulation and the implementation of systems. Ultimately, a set of bit-plane level medical image encryption algorithm is proposed on the basis of MHNNs, which has excellent encryption performance and can effectively protect the privacy of patients.