Parameter space of the Rulkov chaotic neuron model

• We present the parameter space of the two dimensional Rulkov chaotic neuron model.
• We clarify analytically different dynamics and firing regimes of a single neuron.
• We reveal the parameter domains of the quasi-periodic solutions.

The parameter space of the two dimensional Rulkov chaotic neuron model is taken into account by using the qualitative analysis, the co-dimension 2 bifurcation, the center manifold theorem, and the normal form. The goal is intended to clarify analytically different dynamics and firing regimes of a single neuron in a two dimensional parameter space. Our research demonstrates the origin that there exist very rich nonlinear dynamics and complex biological firing regimes lies in different domains and their boundary curves in the two dimensional parameter plane. We present the parameter domains of fixed points, the saddle-node bifurcation, the supercritical/subcritical Neimark–Sacker bifurcation, stability conditions of non hyperbolic fixed points and quasiperiodic solutions. Based on these parameter domains, it is easy to know that the Rulkov chaotic neuron model can produce what kinds of firing regimes as well as their transition mechanisms. These results are very useful for building-up a large-scale neuron network with different biological functional roles and cognitive activities, especially in establishing some specific neuron network models of neurological diseases.