Cross-Hemispheric Memristive Neural Network With Discrete Corsage Memristor for Telemedicine Encryption Application
Haodong Li, Fuhong Min
Abstract
The dynamics of memristive neural networks have incurred significant concern. However, existing research predominantly concentrates on networks with single or multiple neurons, ignoring the large-scale and high-dimensional characteristics inherent to brain networks. To this end, this article first innovatively introduces a family of discrete corsage memristor (DCM) models and scrutinizes their attributes, including nonvolatility, local activity, and edge of chaos. Subsequently, a cross-hemispheric memristive neural network (CMNN) model with dual ring-star structure is proposed, enabling intra-hemispheric information exchange via bidirectional electrical synapses and inter-hemispheric communication through shared memristor synapses. Furthermore, the spatio-temporal dynamics of the CMNN are numerically investigated with the aid of quantitative metrics and visualization techniques, exhibiting phase chimera and complete interlayer synchronization. In particular, the synchronization condition is theoretically estimated utilizing the Lyapunov stability theorem. Moreover, we develop a microcontroller unit (MCU)-based hardware experiment platform to implement the CMNN. Finally, a telemedicine encryption scheme is developed for safeguarding medical images in the Internet of Medical Things (IoMT), with experimental results validating its reliability and security performance.