Dynamics effects of bias current composed on inertial neural system: multistability control and application in image encryption

The function of the biological nervous system is related to its dynamics. This paper explored the dynamics effects of bias current composed on inertial neural system based with two neurons. The model affected by the bias current can induce a reduction in the number of equilibrium points, the number of coexistence attractors, as well as the disturbance of the symmetry of this model compared to the model in without bias current. In absence of bias current we report multistability of up to six different attractors, symmetry birth of chaos via period-bubbling cascades in are reported in the model. Coexistence of symmetry bursting oscillations, parallel branch, and hysteresis dynamic are also presented in the system. The presence of bias current control the symmetry of the model and generates complex phenomena among others, coexistence of five asymmetric attractors, coexistence of asymmetric bubble and asymmetric bursting oscillation. Equilibrium point and Hopf bifurcation are perform in the paper. Furthermore, control of multistability is used for future application in engineering. Security analyses indicates that the proposed image encryption scheme exhibits a good encryption performance and can withstand known attacks.