Effects of external stimuli on the dynamics of deterministic and stochastic Hindmarsh–Rose neuron models

We investigate the nonlinear dynamics of the deterministic and stochastic Hindmarsh–Rose (HR) neuron models under the influence of an external sinusoidal electric current and magnetic flow effects. In the deterministic regime, in the absence of a magnetic field, we observe multistability between periodic and chaotic attractors. This is accompanied by the emergence of self-similar windows of chaotic dynamics that converge within a broad domain of periodic dynamics in parameter space. Introducing a magnetic flux partially suppresses chaotic dynamics while maintaining multistability. Under stochastic conditions due to the introduction of Gaussian noise with arbitrarily small intensity, D, noise triggers transitions between coexisting states, exhibiting a preference for specific attractors from the deterministic case without returning to any other coexisting metastable states. By increasing D and appropriately adjusting the remaining control parameters of the HR neuron model, it becomes feasible to achieve regimes of noise-induced chaos or noise-induced stabilization, effectively suppressing chaotic dynamics. Furthermore, within this framework, we explore the existence of transient chaotic dynamics.