The Institute of Automation of the Chinese Academy of Sciences, in collaboration with Tsinghua University, Peking University, and other institutions, has proposed a method for constructing brain-like neuron models based on endogenous complexity. This method demonstrates the equivalence of the dynamic characteristics of the LIF model and HH model in spiking neural networks and enhances the endogenous complexity of the computational unit through microarchitecture design, allowing the HH network model to simulate larger-scale characteristics of the LIF network model. The research team has also simplified the model to the s-LIF2HH model, and validated its ability to capture complex dynamic behaviors through simulation experiments.