Further study on the Rulkov neuron: geometric control, circuit realization and chaos-based encryption

Abstract

Rulkov neuron with various firing modes is exhaustively explored by numerical simulation aiming to disclose its unique properties of evolution. Transient chaos and the coexistence of periodic state and chaos are found. Furthermore, it is found that the chaotic attractors in the neuron can also be arranged flexibly in phase space by a single offset booster, while the amplitude of firings is rescaled by an independent amplitude controller. Circuit implementation based on MCU is carried out demonstrating the predicted dynamics. Confidentiality and security of data play an important role in safeguarding national production and livelihood. In this paper, the chaotic firing is introduced into the Tiny Encryption Algorithm (TEA) for image encryption based on MCU combined with metrics analysis. It is proven that the Chaos-based Tiny Encryption Algorithm (ChaosTEA) exhibits higher efficiency and security compared to the traditional TEA algorithm.