Firing pattern transition of fractional-order memristor-coupled Hindmarsh–Rose neurons model and its medical image encryption for region of interest

This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of the system is revealed. In order to deeply expose information transmission in neural networks, the bifurcation behavior of different neuronal orders is studied by three dimensional two-parameter bifurcation diagram. Furthermore, when external stimulus is applied to a neuron, the system produces anti-monotonicity and bursting behavior. A microcontroller based on ARM is used to implement the system and verify various firing activities. Finally, we use the properties of the chaotic system to design a medical image encryption algorithm based on the region of interest. Numerical simulation results demonstrate that the proposed algorithm can improve the security of medical image transmission and resource utilization. It provides strong resistance against attacks to ensure privacy.