Dual Memristor-Coupled Unidirectional Ring Neural Network: Abundant Hidden Firings and Application in Hardware Image Encryption

Due to the fixed weights and simple topology of traditional unidirectional ring neural networks, it is challenging to generate complex firing dynamics. This paper introduces two threshold memristor models to mimic neuronal autapses, constructing a novel dual memristor-coupled unidirectional ring neural network (DMCURNN). Benefiting from the dual memristor-based autapse emulation, the DMCURNN enriches diverse hidden firing dynamics that are absent in conventional unidirectional ring neural networks, and the richness and complexity of these hidden firing dynamics are highly sensitive to the memristive coupling strengths. Numerical simulations indicate that the DMCURNN is able to generate various hidden firing activities, pattern transitions, homogeneous firing multistability of infinite coexisting homogeneous firing patterns and heterogeneous firing multistability of seven heterogeneous firing patterns. Furthermore, through adjusting the coupling parameters along with the external current intensity, the regulatory mechanisms of hidden firing patterns and their amplitude and frequency regulation are revealed. Subsequently, the hidden firing dynamics of this network are verified on an FPGA hardware platform. Finally, Using the hidden firing multistability characteristic of the DMCURNN, a novel bitstream-based hardware image encryption scheme with a dynamic key update mechanism is designed. Performance evaluations demonstrate that the proposed scheme exhibits exceptional robustness and high efficiency for IoT security applications.