Periodic firing and stability analysis of FHN neuron model and its application in image encryption

The rich discharge characteristics and information processing capabilities of neurons can be applied to information encryption. The firing patterns of the three-dimensional FitzHugh–Nagumo (FHN) neuron model under periodic excitation is investigated by employing the discrete mapping method. The different types of bifurcations that the FHN neuron undergoes when its motion becomes chaotic under periodic excitation are analyzed. The results show that system dynamics vary with excitation parameter changes. Neuron discharge behavior is more sensitive to initial values in a chaotic state. The impact of periodic excitation parameters on system dynamics is investigated, revealing stable and chaotic regions of the model. Finally, an image encryption application based on the FHN neuron is developed. When the neuron system is chaotic, this method offers higher key sensitivity, providing a new approach to chaotic encryption design.