Chaoming Wang
Ph.D., Principal Investigator
Email：wangchaoming@@gdiist.cn
Personal Profile
Dr. Chaoming Wang is the Principal Investigator of the Laboratory for Large-Scale Brain Simulation. He received his Bachelor's degree from Beijing Jiaotong University in 2018 and his Ph.D. degree from Peking University in 2023. From 2023 to 2025, he served as a Boya Postdoctoral Fellow at Peking University. He has been the principal investigator for projects funded by the National Natural Science Foundation of China (Youth Program) and the China Postdoctoral Science Foundation (General Program). Dr. Wang's research focuses on the software, algorithms, and models for large-scale brain simulation. His related research has been published in over 10 high-level journals and top-tier AI conferences, including Nature Communications, eLife, Cell Reports, and ICLR, and he has been granted multiple national invention patents.
He has led the development of several original Chinese brain dynamics modeling frameworks, including: (1) the BrainPy framework, which implements a just-in-time compiled multi-scale brain dynamics programming system, significantly improving the computational efficiency of brain modeling; (2) the BrainUnit/SAIUnit system, which introduces standardized physical units into high-performance AI computing architectures, substantially enhancing the accuracy, maintainability, and physical interpretability of brain modeling; (3) the BrainTaichi framework, which provides sparse computation and event-driven operator programming capabilities, effectively reducing computational complexity; and (4) the BrainScale system, which achieves online learning of spiking neural networks with linear memory complexity O(N). These tools collectively form a multi-scale, differentiable modeling toolchain, realizing the deep integration of AI computing frameworks and brain simulation techniques.
Furthermore, his research work have been supported by the National Science and Technology Innovation 2030-Brain Science and Brain-inspired Intelligence Project and have received the OpenI Community Outstanding Project Award for two consecutive years (2021/2022).
Laboratory for Large-Scale Brain Simulation
Our laboratory is dedicated to advancing understanding of brain functions by constructing multi-scale, high-precision simulation platforms and models -- from individual neurons to brain circuits to whole-brain networks, thereby revealing the core mechanisms of cognition and intelligence. Specifically, by advancing the underlying software, algorithms, theories, and models of large-scale brain simulation, our goal is to deliver full-scale, biologically realistic yet functional whole-brain models.
To this end, we pursue advanced methods for modeling large-scale brain networks and build high-performance, parallel computing frameworks to simulate and analyze complex neural dynamics. By uniting computational neuroscience, high-performance computing, and artificial intelligence, we drive end-to-end simulations—from individual neuron activity through large-scale network interactions to emergent cognitive behaviors—providing the engineering tools and theoretical foundations needed both to decode brain function and to inspire next-generation, brain-inspired intelligence.
Currently, the core research directions of our group include:
1、Software Ecosystem for Large-Scale Brain Simulation: We are developing efficient parallel algorithms and architectures for distributed brain modeling, leveraging our previous BrainPy, BrainUnit/SAIUnit, BrainTaichi, and BrainScale programming frameworks. This effort aims to construct simulation platforms compatible with heterogeneous computing systems, ultimately overcoming current limitations in simulation scale to support whole-brain dynamics simulations involving hundreds of billions of neurons. This will provide robust tools for the in-depth analysis of complex brain activity.
2、Modeling Algorithms for Large-Scale Brain Simulation: Leveraging our BrainScale online learning system, we are developing resource-optimized algorithms for whole-brain scale simulation, learning mechanisms that mimic the brain's modular cognitive functions, and "data + task" dual-driven multi-scale brain modeling approaches. The goal of this research is to elucidate the complex interplay between brain structure, neural activity, and cognitive function, thereby establishing an engineering and potentially theoretical framework for understanding the neural underpinnings of intelligence.
3、Computational Models for Large-Scale Brain Simulation: Building upon the large-scale brain simulation software and algorithms that we previously developed, and integrating diverse multi-modal neuroscience data, we are committed to constructing multi-level large-scale brain dynamics models of vertebrates, spanning molecules, cells, circuits, and systems. Furthermore, we intend to apply these models to investigate the mechanisms of brain diseases and to facilitate the development of innovative biologically compatible artificial intelligence algorithms, thereby advancing the applied research of large-scale brain simulation.
Our research group's official GitHub account is https://github.com/chaobrain, where all software, algorithms, and models related to large-scale brain simulation are completely open-source, aiming to promote open collaboration and technology sharing in this field.
The research group possesses advanced computational resources, establishing close collaborative relationships with leading research institutions both domestically and internationally. We welcome talented individuals from fields including neural computation, artificial intelligence, and high-performance computing to apply for positions as Assistant Research Fellows, Postdoctoral Researchers, Engineers, Doctoral Students, Master's Students, or Interns.
Selected Publications
1. Chaoming Wang, Xingsi Dong, Jiedong Jiang, Zilong Ji, Xiao Liu, and Si Wu. “BrainScale: Enabling Scalable Online Learning in Spiking Neural Networks”. Preprint (2025).
2. Chaoming Wang#, Sichao He#, Shouwei Luo, Yuxiang Huan, Si Wu. “Integrating physical units into high-performance AI-driven scientific computing”. In: Nature Communications 16 (2025). https://doi.org/10.1038/s41467-025-58626-4
3. Shangyang Li#, Chaoming Wang#, and Si Wu. “Spindle oscillations emerge at the critical state of electrically coupled networks in the thalamic reticular nucleus”. In: Cell Reports 43.10 (2024). https://doi.org/10.1016/j.celrep.2024.114790
4. Chaoming Wang#, Tianqiu Zhang#, Sichao He, Yifeng Gong, Hongyaoxing Gu, Shangyang Li, and Si Wu. “A differentiable brain simulator bridging brain simulation and brain-inspired computing”. In: The Twelfth International Conference on Learning Representations (2024). https://openreview.net/forum?id=AU2gS9ut61
5. Chaoming Wang*, Muyang Lyu, Tianqiu Zhang, Si Wu*. “A Differentiable Approach to Multi-scale Brain Modeling”. In: ICML 2024 Workshop on Differentiable Almost Everything. 2024. https://openreview.net/forum?id=a6cpnxdGq7
6. Ke Yang, Yanghao Wang, Pek Jun Tiw, Chaoming Wang, Xiaolong Zou, Rui Yuan, Chang Liu, Ge Li, Chen Ge, Si Wu, Teng Zhang, Ru Huang, Yuchao Yang. “High-order sensory processing nanocircuit based on coupled VO2 oscillators”. In: Nature Communications 15.1 (2024). https://doi.org/10.1038/s41467-024-45992-8
7. Chaoming Wang, Tianqiu Zhang, Xiaoyu Chen, Sichao He, Shangyang Li, Si Wu. “BrainPy, a flexible, integrative, efficient, and extensible framework for general-purpose brain dynamics programming”. In: eLife 12 (2023). Ed. by Marcel Stimberg. https://doi.org/10.7554/eLife.86365
8. 王超名, 陈啸宇, 张天秋, 吴思. 神经计算建模实战：基于 BrainPy. 电子工业出版社 (2023年年度好书), 2023. https://book.douban.com/subject/36437736/
9. Chaoming Wang, Yingqian Jiang, Xinyu Liu, Xiaohan Lin, Xiaolong Zou, Zilong Ji, Si Wu. “A just-in-time compilation approach for neural dynamics simulation”. In: Neural Information Processing: 28th International Conference, 2021. https://doi.org/10.1007/978-3-030-92238-2_2
10. Chaoming Wang, Shangyang Li, and Si Wu. “Analysis of the Neuron Dynamics in Thalamic Reticular Nucleus by a Reduced Model”. In: Frontiers in Computational Neuroscience 15 (2021). https://doi.org/10.3389/fncom.2021.764153
11. Chaoming Wang, Risheng Lian, Xingsi Dong, Yuanyuan Mi, and Si Wu. “A Neural Network Model With Gap Junction for Topological Detection”. In: Frontiers in Computational Neuroscience 14 (2020). https://doi.org/10.3389/fncom.2020.571982
