Building more universal artificial intelligence and enabling models to have broader and more universal cognitive abilities is an important goal in the current development of artificial intelligence (AI). Improving the computational resource consumption caused by the expansion of traditional models to external scales is urgent. Li Guoqi and Xu Bo from the Institute of Automation of the Chinese Academy of Sciences, together with Tsinghua University, Peking University and other universities, have proposed a construction method of brain like neuron model "based on endogenous complexity", which provides an example for improving computing resource consumption and effectively using neuroscience to develop artificial intelligence. This work was published in Nature Computational Science. The current popular approach for large model paths is to build larger, deeper, and wider neural networks based on the Scaling Law, which can be referred to as a general intelligent implementation method based on exogenous complexity. This path faces problems such as unsustainable consumption of computing resources and energy, and insufficient interpretability. This study first demonstrated the equivalence of the dynamic characteristics between the pulse neural network neuron LIF model and the HH model, and further theoretically proved that the HH neuron can be equivalent to the dynamic characteristics of four time-varying parameter LIF neurons (tv LIF) with specific connection structures. It is reported that the HH model, also known as the Hodgkin Huxley model, was proposed by British physiologists Alan Hodgkin and Andrew Huxley in 1952 based on electrophysiological experimental data of squid giant axons. It was used to describe the generation and transmission of neural pulses and was awarded the Nobel Prize in Medicine or Physiology in 1963. This model is an important milestone in the field of neuroscience, as it first explains the mechanism of action potential generation at the molecular level, laying the foundation for subsequent neuronal electrophysiological research. Based on this equivalence, the team improved the endogenous complexity of the computing unit by designing a microarchitecture, enabling the HH network model to simulate the dynamic characteristics of larger scale LIF network models and achieve similar computing functions on a smaller network architecture. Furthermore, the team simplified the "HH model" (tv LIF2HH) constructed by four tv LIF neurons into an s-LIF2HH model, and verified the effectiveness of this simplified model in capturing complex dynamic behaviors through simulation experiments. The experimental results show that the HH network model and the s-LIF2HH network model have similar performance in terms of representation ability and robustness, verifying the effectiveness and reliability of the endogenous complexity model in handling complex tasks. Meanwhile, research has found that the HH network model is more efficient in terms of computational resource consumption, significantly reducing the use of memory and computation time, thereby improving overall computational efficiency. The research team explained the above research results through the information bottleneck theory. This study provides new methods and theoretical support for integrating the complex dynamic characteristics of neuroscience into artificial intelligence, and offers feasible solutions for optimizing and improving the performance of AI models in practical applications. At present, the research team has conducted research on larger scale HH networks and multi branch multi chamber neurons with greater endogenous complexity, which is expected to further improve the computational efficiency and task processing capabilities of large models, and achieve rapid implementation in practical application scenarios. Li Guoqi, Researcher of Institute of Automation, Chinese Academy of Sciences, Xu Bo, Researcher, and Tian Yonghong, Professor of Peking University are the co corresponding authors of this paper. The co first authors are He Linxuan (intern at the Institute of Automation), an undergraduate student in the Qian Xuesen class at Tsinghua University, Xu Yunhui (intern at the Institute of Automation), and He Weihua and Lin Yihan, doctoral students in the Department of Precision and Instrumentation at Tsinghua University. (New Society)