Stability and complexity evaluation of attractors in a controllable piezoelectric Fitzhugh-Nagumo circuit

Chaotic systems have potential applications in secure communication and image encryption. The FitzHughNagumo (FHN) neuron circuit model, as one of the most important models for neuron modelling, has a good chaotic discharge mode. On this basis, a controllable FHN piezoelectric neuron circuit is designed in this paper. By adjusting the state of the switch on or off, the circuit model can produce three different operating modes. The results show that the dynamic state of the system is determined by external stimuli, internal parameters of the system, and the number and nature of the external driving sources. More drivers can provide more chaos parameters. By converting different working modes, we can generate chaotic sequences with high randomness under different parameters. We found that when multiple drivers work together, the system output is more complex, and there is competition between dynamics induced by different drivers in the system. The addition of chaotic current makes the output discharge of the system produce chaotic resonance and pseudo-chaotic mode, which greatly improves the complexity of the sequence. This provides a new method for generating chaotic sequences with high randomness. Our work lays a theoretical foundation for the optimization of chaotic encryption algorithms in the future. In the future, we will further explore the effects of chaotic current stimulation with different properties. We hope that these findings will provide new insights into the security of information communications.