Hidden firing patterns and memristor initial condition-offset boosting behavior in a memristive Hindmarsh-Rose neuron model - Fan, Weiwei; Chen, Xiongjian; Wang, Yiteng; Chen, Bei; Wu, Huagan; Xu, Quan (Frontiers in Physics, 2023)
Electromagnetic induction can effectively induce abundant firing patterns in neurons. In modeling a neuron model with electromagnetic induction effect, an electromagnetic induction current is frequently added to the state equation of membrane potential. To more properly reflect the non-uniform distribution of the ions inside and outside neuron membrane, an ideal flux-controlled memristor with sinusoidal memductance function and nonlinearly modulated input is raised to depict electromagnetic induction effect on a Hindmarsh-Rose neuron model, thereby a three-dimensional (3-D) memristive Hindmarsh-Rose (mHR) neuron model is built in this paper. The proposed mHR neuron model possesses no equilibrium point since the involvement of the ideal flux-controlled memristor, which induces the generation of hidden dynamics. Numerical results declare that the mHR neuron model can generate abundant hidden dynamics, i.e. periodic spiking, chaotic spiking, period-doubling bifurcation route, tangent bifurcation, and chaos crisis. These hidden dynamics are very related to memristor coupling strength and externally applied stimulus. Afterward, memristor initial condition-offset boosting behavior is revealed. This can trigger the generation of infinite multiple coexisting firing patterns along the memristor variable coordinate. These coexisting firing patterns have identical attractor topology but different locations in the phase plane. Finally, an analog circuit is designed for implementing the mHR neuron model and PSIM-based circuit simulation is executed. The circuit simulated results perfectly verify the generation of hidden infinite multiple coexisting initial condition-offset boosting firing patterns in the proposed mHR neuron model.