Reconfigurable Multiscroll Memristive Neural Network With Application to Telemedicine Privacy Protection

Constructing memristive neural networks (MNNs) with multiscroll chaotic attractors helps advance both theoretical and applied research on neural networks. However, the existing models mainly utilize complex memristor models with polynomial functions, nested composite functions, and so on, to generate multiscroll chaotic attractors, which leads to increased model complexity and difficulties in on-demand adjustment. Hence, this article proposes a reconfigurable multiscroll MNN (RMMNN) that can yield different types of multiscroll chaotic attractors merely by altering the memristive parameters without modifying its model. Through numerical methods, the complex dynamics of the RMMNN in different cases are analyzed, such as parameter-controlled multiscroll chaotic attractors, adjustable multistability, and parameter-induced transitions of multistability. In addition, the reliability of the numerical analysis is verified via the hardware circuits. Moreover, to address the issues of image security and low quality in telemedicine, a bidirectional rotation medical image encryption scheme (BRMIES) is developed based on the good pseudorandom chaotic sequences generated by RMMNN. Performance analysis demonstrates that BRMIES can effectively protect medical image and robustly handle various potential adverse interferences within telemedicine process.