A Novel Discrete Memristor-Coupled Heterogeneous Dual-Neuron Model and Its Application in Multi-Scenario Image Encryption

TL;DR

This paper introduces a novel memristor-coupled dual-neuron network model, analyzes its stability and dynamics, and applies it to develop real-time, hardware-implemented image encryption for secure police data transmission.

Contribution

It presents a new discrete memristive dual-neuron model, analyzes its stability and dynamics, and demonstrates its application in multi-scenario secure image encryption with hardware validation.

Findings

The MHDNN exhibits complex dynamical behaviors and various firing patterns.

Synchronization phenomena are observed under different coupling strengths.

The hardware implementation enables real-time, secure police data encryption.

Abstract

Simulating brain functions using neural networks is an important area of research. Recently, discrete memristor-coupled neurons have attracted significant attention, as memristors effectively mimic synaptic behavior, which is essential for learning and memory. This highlights the biological relevance of such models. This study introduces a discrete memristive heterogeneous dual-neuron network (MHDNN). The stability of the MHDNN is analyzed with respect to initial conditions and a range of neuronal parameters. Numerical simulations demonstrate complex dynamical behaviors. Various neuronal firing patterns are investigated under different coupling strengths, and synchronization phenomena between neurons are explored. The MHDNN is implemented and validated on the STM32 hardware platform. An image encryption algorithm based on the MHDNN is proposed, along with two hardware platforms tailored for multi-scenario police image encryption. These solutions enable real-time and secure transmission of police data in complex environments, reducing hacking risks and enhancing system security.