Creating an artificial being towards and beyond Human-level Intelligence is the final goal of Intelligence Science. Until now, except for human being, there are no intelligent system which owns the cognitive capability of multi-modal sensation and perception, learning, decision making, reasoning, prediction, language processing, and movement at the same time, very robust, and can keep on working for several decades. Hence, investigations on how the brain and the mind works, and creat cognitive brain computational model for the advances of intelligent systems is an important approach for realizing Human-level Artificial Intelligence. At the same time, we need to ensure potential risks, safety and ethical issues during the development of Artificial Intelligence to make sure it is going for social good.

My reasearch focuses on Brain-inspired Artificial Intelligence, AI Ethics and Governance. 

(1) Brain-inspired Artificial Intelligence: Brain-inspired Spiking Neural Networks, Brain-inspired autonomous-learning theory, and their applications to intelligent information processing and Cognitive Robotics.

(2) AI Ethics and Governance: Risks, Safety and Ethical issues for AI, and development of Low Risk, Safe, and Ethical AI models and applications.

Ph.D

• Expert, Ad Hoc Expert Group on AI Ethics, United Nations Education, Science and Culture Organization (UNESCO). (2020-).

• Expert, World Health Organization (WHO) Expert Group on Ethics and Governance of Artificial Intelligence in Health (2019-).

• Committee Member, National Governance Committee of Next Generation Artificial Intelligence, Ministry of Science and Technology China. (2019-).

• Invited Expert, Research Center for Next Generation Artificial Intelligence Development, Ministry of Science and Technology China. (2018-).

• Committee Member, Global Future Council on China. World Economic Forum. (2019-2020)

• Committee Member, Global Future Council on Values, Ethics and Innovation. World Economic Forum. (2018-2019)

Professor, 
Deputy Director, Research Center for Brain-inspired Intelligence, 

Director, International Research Center for AI Ethics and Governance,

Institute of Automation, Chinese Academy of Science

• Introduction to Brain-inspired Intelligence [2017-2021], University of Chinese Academy of Sciences
• System and Computational Neuroscience [2018-2021], University of Chinese Academy of Sciences
• Philosophy and Ethics of Artificial Intelligence [2018-2021], University of Chinese Academy of Sciences

   

• Zhuoya Zhao, Enmeng Lu, Feifei Zhao, Yi Zeng, and Yuxuan Zhao. A Brain-Inspired Theory of Mind Spiking Neural Network for Reducing Safety Risks of Other Agents. Frontiers in Neuromorphic Engineering, 2022.

• Yang Li, Yi Zeng. Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes. Proceedings of the 31st International Joint Conferences on Artificial Intelligence (IJCAI-22), 2022.

• Yuwei Wang, Yi Zeng. Multisensory Concept Learning Framework Based on Spiking Neural Networks. Frontiers in Systems Neuroscience, 16:845177, 2022.

• Yuwei Wang, Yi Zeng. Statistical Analysis of Multisensory and Text-Derived Representations on Concept Learning. Frontiers in Computational Neuroscience, 16:861265, 2022.

• Hongjian Fang, Yi Zeng. A Brain-Inspired Causal Reasoning Model Based on Spiking Neural Networks. 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021.

• Yinqian Sun，Yi Zeng，Tielin Zhang. Quantum Superposition Inspired Spiking Neural Network. iScience, 24(8), 102880, Cell Press, 2021.

• Yuxuan Zhao, Yi Zeng, Guang Qiao. Brain-Inspired Classical Conditioning Model. iScience, 24(1), 101980, Cell Press, 2021.

• Yang Li, Yiting Dong, Dongcheng Zhao, Yi Zeng. N-Omniglot: a Large-scale Neuromorphic Dataset for Spatio-Temporal Sparse Few-shot Learning. Arxiv:2112.13230, 2021.

• Tielin Zhang, Xiang Cheng, Shuncheng Jia, Mu-Ming Poo, Yi Zeng, Bo Xu. Self-backpropagation of synaptic modifications elevates the efficiency of spiking and artificial neural networks. Science Advances, 7(43), AAAS, 2021.

• Feifei Zhao, Yi Zeng, Jun Bai. Toward a Brain-Inspired Developmental Neural Network Based on Dendritic Spine Dynamics. Neural Computation, MIT Press, 2021.

• Qian Liang, Yi Zeng. Stylistic Composition of Melodies based on a Brain-inspired Spiking Neural Network. Frontiers in Systems Neuroscience, 15:639484, 2021.

• Feifei Zhao, Yi Zeng. Dynamically Optimizing Network Structure Based on Synaptic Pruning in the Brain. Frontiers in Systems Neuroscience, 15:620558, 2021.

• Dongcheng Zhao, Yi Zeng, Yang Li. BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons. arXiv:2105.13004, 2021.

• Yang Li, Yi Zeng, Dongcheng Zhao. BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neurons. arXiv:2105.12917, 2021.

• Hongjian Fang, Yi Zeng, Feifei Zhao. Brain Inspired Sequences Production by Spiking Neural Networks With Reward-Modulated STDP. Frontiers in Computational Neuroscience, 15:612041, 2021.

• Dongcheng Zhao, Yi Zeng, Tielin Zhang, Mengting Shi, Feifei Zhao. GLSNN: A Multi-layer Spiking Neural Network based on Global Feedback Alignment and Local STDP Plasticity. Frontiers in Computational Neuroscience, 2020.

• Yi Zeng, Yuxuan Zhao, Tielin Zhang, Dongcheng Zhao, Feifei Zhao, and Enmeng Lu. A Brain-Inspired Model of Theory of Mind. Frontiers in Neurorobotics, 2020.

• Qian Liang, Yi Zeng, Bo Xu. Temporal-Sequential Learning with a Brain-Inspired Spiking Neural Network and Its Application to Musical Memory. Frontiers in Computational Neuroscience, 14:51, 2020.

• Mengting Shi, Tielin Zhang, Yi Zeng. A Curiosity-Based Learning Method for Spiking Neural Networks. Frontiers in Computational Neuroscience, 2020.

• Feifei Zhao, Qingqun Kong, Yi Zeng, Bo Xu. A Brain-inspired Visual Fear Responses Model for UAV Emergent Obstacle Dodging. IEEE Transactions on Cognitive and Developmental Systems, 12(1): 124-132, 2020.

• Tielin Zhang, Yang Yang, Yi Zeng, Yuxuan Zhao. Cognitive Template-clustering Improved LineMod for Efficient Multi-object Pose Estimation. Cognitive Computation, 2020.

• Yuwei Wang, Yi Zeng, Jianbo Tang and Bo Xu. Biological Neuron Coding Inspired Binary Word Embeddings. Cognitive Computation, Springer, 2019.

• Jun Bai, Yi Zeng, Yuxuan Zhao and Feifei Zhao. Training a V1 Like Layer Using Gabor Filters in Convolutional Neural Networks. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN 2019). Budapest, Hungary, July 14-19, 2019.

• Dongcheng Zhao, Yi Zeng. Dynamic Fusion of Convolutional Features based on Spatial and Temporal Attention for Visual Tracking. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN 2019). Budapest, Hungary, July 14-19, 2019.

• Feifei Zhao, Yi Zeng, and Bo Xu. A Brain-inspired Decision-Making Spiking Neural Network and Its Application in Unmanned Aerial Vehicle. Frontiers in Neurorobotics, 2018.

• Tielin Zhang, Yi Zeng, Dongcheng Zhao, Bo Xu. Brain-inspired Balanced Tuning for Spiking Neural Networks. The 27th International Joint Conference on Artificial Intelligence (IJCAI-18), Stockholm, Sweden, 2018.

• Yi Zeng, Guixiang Wang, Bo Xu. A Basal Ganglia Network Centric Reinforcement Learning Model and Its Application in Unmanned Aerial Vehicle. IEEE Transactions on Cognitive and Developmental Systems, 10(2): 290-303, 2018.

• Yi Zeng, Yuxuan Zhao, Jun Bai, Bo Xu. Towards Robot Self-consciousness (II): Brain-inspired Robot Bodily Self Model for Self-Recognition. Cognitive Computation, 10(2): 307-320, Springer, 2018.

• Tielin Zhang, Yi Zeng, Dongcheng Zhao, Mengting Shi. A Plasticity-centric Approach to Train the Non-differential Spiking Neural Networks. The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), Lousiana, USA, 2018.

• Yi Zeng, Tielin Zhang, Bo Xu. Improving Multi-layer Spiking Neural Networks by Incorporating Brain-inspired Rules. SCIENCE CHINA Information Sciences, 60(5): 052201, 2017.

• Feifei Zhao, Tielin Zhang, Yi Zeng, Bo Xu. Towards a Brain-inspired Developmental Neural Network by Adaptive Synaptic Pruning. Proceedings of the 24th International Conference on Neural Information Processing (ICONIP 2017), 2017.

• Guang Qiao, Hongyue Du, Yi Zeng. A Quaternionic Rate-based Synaptic Learning Rule Derived from Spike-Timing Dependent Plasticity. Lecture Notes in Computer Science, 10261, 457-465, 2017.

• Xin Liu, Yi Zeng, Bo Xu. Parallel Brain Simulator: A Multi-scale and Parallel Brain-inspired Neural Network Modeling and Simulation Platform. Cognitive Computation, Volume 8, Issue 5, 967-981, Springer, 2016.

• Yi Zeng, Yuxuan Zhao, Jun Bai. Towards Robot Self-Consciousness (I): Brain-inspired Robot Mirror Neuron System Model and Its Application in Mirror Self-Recognition. Lecture Notes in Computer Science, 10023, 11-21, 2016.

• Guixiang Wang, Yi Zeng, Bo Xu. A Spiking Neural Network Based Autonomous Reinforcement Learning Model and Its Application in Decision Making. Lecture Notes in Computer Science, 10023, 125-137, 2016.

• Yi Zeng, Chenglin Liu, Tieniu Tan. Retrospect and Outlook of Brain-inspired Intelligence Research [in Chinese]. The Chinese Journal of Computers, Volume 39, Issue 1, 212-222, 2016.

• Bo Xu, Chenglin Liu, Yi Zeng. Research Status and Developments of Brain-inspired Intelligence [in Chinese]. Bulletin of Chinese Academy of Sciences, Volume 31, Issue 7, 793-802, 2016.

• Tielin Zhang, Yi Zeng, and Bo Xu. HCNN: A Neural Network Model for Combining Local and Global Features towards Human-like Classification. International Journal of Pattern Recognition and Artificial Intelligence, Volume 30, Issue 1, 1655004, 2016.

• Yi Zeng, Feifei Zhao, Guixiang Wang, Lingyu Zhang, Bo Xu. Brain-inspired Obstacle Detection based on the Biological Visual Pathway. Proceedings of the 2016 International Conference on Brain Informatics and Health, Lecture Notes in Artificial Intelligence 9919, Omaha, Nebraska, USA, 2016.

• Yuwei Wang, Yi Zeng, Bo Xu. SHTM: A Neocortex-inspired Algorithm for One-shot Text Generation. Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics, 898-903, IEEE Press, Budapest, Hungary, October 9-12, 2016.

• Tielin Zhang, Yi Zeng, Dongcheng Zhao, Liwei Wang, Yuxuan Zhao, Bo Xu. HMSNN: Hippocampus inspired Memory Spiking Neural Network. Proceedings of the 2016 IEEE International Conference on Systems, Man, and Cybernetics, 2301-2306, IEEE Press, Budapest, Hungary, October 9-12, 2016.

• Xiaomei kang, Qingqun Kong, Yi Zeng, Bo Xu. A Fast Contour Detection Model Inspired by Biological Mechanisms in Primary Vision System. Frontiers in Computational Neuroscience, 2018.

• Feifei Zhao, Yi Zeng, Guixiang Wang, Jun Bai, Bo Xu. A Brain-inspired Decision Making Model based on Top-down Biasing of Prefrontal Cortex to Basal Ganglia and Its Application in Autonomous UAV Explorations. Cognitive Computation, 10(2): 296-306, Springer, 2018.

• Chaoming Wang, Yi Zeng. Network of Recurrent Neural Networks: Design for Emergence. Proceedings of the 25th International Conference on Neural Information Processing (ICONIP2018).

• Qingqun Kong, Yi Zeng, Qiulei Dong. Biologically Inspired Deep Stereo Model. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP 2015), 3700-3704, Quebec City, Canada, 2015.

• Ethical Principles and Specifications

• Principles on AI for Climate Actions, 2022. [Yi Zeng, as the lead author, with contributions from Cunqing Huangfu, Kang Sun, Yang Yu, Emma Ruttkamp-Bloem, Mark Findlay, Christoph Lütge, Helen Meng, etc.]

• Ethical Norms for the New Generation Artificial Intelligence, 2021. [Yi Zeng as one of the authors and board members for national governance committee of new generation AI]

• Recommendation of AI Ethics, UNESCO, 2021. [Yi Zeng as one of the members of the UNESCO Adhoc Expert Group on AI Ethics]

• WHO Guidance on AI Ethics and Governance for Health, 2021 [Yi Zeng as one of the members for the WHO Expert Group on AI Ethics and Governance for Health]

• Ethical considerations to guide the use of digital proximity tracking technologies for COVID-19 contact tracing, World Health Organization (WHO), 2020. [Yi Zeng as one of the members for the WHO Expert Group on AI Ethics and Governance for Health.

• Governance Principles for the New Generation Artificial Intelligence, 2019. [Yi Zeng as one of the main authors and board members for the national governance committee of new generation AI]

• AI for Children: Beijing Principles, 2020. [Yi Zeng, Kang Sun, Enmeng Lu as the authors and endorsed by the academic and industrial community]

• Beijing AI Principles, 2019. [Yi Zeng as the lead author, with participation from Enmeng Lu, and endorsed by the academic and industrial community]

• Harmonious Artificial Intelligence Principles, 2018. [Yi Zeng as the author]

• Papers and Reports

• Yi Zeng. Deep Coordination among Development, Ethics, and Governance of Science and Technology. China Science Daily, March 22, 2022.

• Yi Zeng, Kang Sun, Enmeng Lu. Declaration on the Ethics of Brain-Computer Interfaces and Augment Intelligence. AI and Ethics, Springer, 2021.

• Yi Zeng. Global Cooperation on Artificial Intelligence is not a Zero-Sum Game.Guangming Daily, 2021.

• Yi Zeng, Kang Sun, Enmeng Lu. Ethics and Governance Perspectives to Fight Against Catastrophic Risks: From COVID-19 to Long-term Safety Issues of Artificial General Intelligence. In: Data, AI Governance, and COVID-19: Medium and Long-term Perspectives for Asia, 2020 (This paper is originally published in Japanese in IPSJ Magazine, Vol. 61, No. 10, October 2020).

• Seán S. ÓhÉigeartaigh, Jess Whittlestone, Yang Liu, Yi Zeng & Zhe Liu. Overcoming Barriers to Cross-cultural Cooperation in AI Ethics and Governance. Philosophy & Technology, 2020.

• Yi Zeng, Enmeng Lu, Kang Sun, Samuel Curtis. Facial Recognition and Public Health. Artificial Intelligence and Healthy Society Survey Series, Beijing Academy of Artificial Intelligence, 2020.

• Yi Zeng. Artificial Intelligence: An Accelerator for United Nations Sustainable Development Goals. Guangming Daily, June 18th, 2020.

• Yi Zeng, Kang Sun. Fighting COVID-19 with AI: efforts and lessons from China. Global Times, March 7th, 2020.

• Yi Zeng, Enmeng Lu, Yinqian Sun, Ruochen Tian. Responsible Facial Recognition and Beyond. 2019.

• Yi Zeng, Enmeng Lu, Cunqing Huangfu. Linking Artificial Intelligence Principles. The AAAI 2019 Safe Artificial Intelligence (AAAI SafeAI-2019) Workshop, January 27, 2019.

• Qian Zhang, Yi Zeng, Tielin Zhang, Taoyi Yang. Comparison Between Human and Rodent Neurons for Persistent Activity Performance: A Biologically Plausible Computational Investigation. Frontiers in System Neuroscience, 2021.

• Tielin Zhang, Yi Zeng, Yue Zhang, Xinhe Zhang, Mengting Shi, Likai Tang, Duzhen Zhang, Bo Xu. Neuron type classification in rat brain based on integrative convolutional and tree-based recurrent neural networks. Scientific Reports, 11: 7291, Nature Publishing Group, 2021.

• Feifei Zhao, Yi Zeng, Aike Guo, Haifeng Su, Bo Xu. A Neural Algorithm for Drosophila Linear and Nonlinear Decision-making. Scientific Reports, 10: 18660, Nature Publishing Group, 2020.

• Qian Zhang, Yi Zeng, Taoyi Yang. Computational Investigation of Contributions from Different Subtypes of Interneurons in Prefrontal Cortex for Information Maintenance. Scientific Reports, 10: 4671, Nature Publishing Group, 2020.

• Cunqing Huangfu, Yi Zeng, Yuwei Wang. Creating Neuroscientific Knowledge Organization System Based on Word Representation and Agglomerative Clustering Algorithm. Frontiers in Neuroinformatics, 2020.

• Hongyin Zhu, Yi Zeng, Dongsheng Wang, Cunqing Huangfu. Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-sequence Learning Model. Frontiers in Human Neuroscience. 2020.

• Qingqun Kong, Jiuqi Han, Yi Zeng, Bo Xu. Efficient Coding Matters in the Organization of the Early Visual System. Neural Networks, Elsevier, 2018.

• Xiaosha Wang, Yangwen Xu, Yuwei Wang, Yi Zeng, Jiacai Zhang, Zhenhua Ling, Yanchao Bi. Representational similarity analysis reveals task-dependent semantic influence of the visual word form area. Scientific Reports, 8(1): 3047, Nature Publishing Group, 2018.

• Tielin Zhang, Yi Zeng, Bo Xu. A Computational Approach towards the Microscale Mouse Brain Connectome from the Mesoscale. Journal of Integrative Neuroscience, 16(3): 291-306, 2017.

• Yi Zeng, Tielin Zhang, Bo Xu. Neural Pathway Prediction based on Multi-neuron Spike Train Data. The Twenty-Third Annual Computational Neuroscience Meeting (CNS 2014), Québec City, Canada, July 26-31, 2014, BMC Neuroscience, 15(Suppl 1): P6, 2014.

• Tielin Zhang, Yi Zeng, Bo Xu. Neural Spike Prediction based on Spreading Activation. The Twenty-Third Annual Computational Neuroscience Meeting (CNS 2014), Québec City, Canada, July 26-31, 2014, BMC Neuroscience, 15(Suppl 1): P7, 2014.

• Yi Zeng, Dongsheng Wang, Hongyin Zhu. User Interests Analysis and Its Application on the Linked Brain Data Platform. Proceedings of the 2016 International Conference on Brain Informatics and Health, Lecture Notes in Artificial Intelligence 9919, Omaha, Nebraska, USA, October 13-16, 2016.

• Hongyin Zhu, Yi Zeng, Dongsheng Wang, Bo Xu. Brain Knowledge Graph Analysis Based on Complex Network Theory. Proceedings of the 2016 International Conference on Brain Informatics and Health, Lecture Notes in Artificial Intelligence 9919, Omaha, Nebraska, USA, October 13-16, 2016.

• Hongyin Zhu, Yi Zeng, Dongsheng Wang, Bo Xu. Relation Inference and Type Identification Based on Brain Knowledge Graph. Proceedings of the 2016 International Conference on Brain Informatics and Health, Lecture Notes in Artificial Intelligence 9919, Omaha, Nebraska, USA, October 13-16, 2016.

• Jiuqi Han, Qingqun Kong, Yi Zeng and Hongwei Hao. HEVS: A Hierarchical Computational Model for Early Stages of the Visual System. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN 2015), 1-8, Killarney, Ireland, July 12-17, 2015.

• Tielin Zhang, Yi Zeng and Bo Xu. Biological Neural Network Structure and Spike Activity Prediction based on Multi-neuron Spike Train Data. International Journal of Intelligence Science, 5(2), 102-111, 2015.

• Yi Zeng, Dongsheng Wang, Tielin Zhang and Bo Xu. Linked Neuron Data (LND): A Platform for Integrating and Semantically Linking Neuroscience Data and Knowledge. Frontiers in Neuroinformatics. Conference Abstract: The 7th Neuroinformatics Congress (Neuroinformatics 2014), Leiden, the Netherlands, August 25-27, 2014.

• Yi Zeng, Weida Bi, Yun Wang, Xuan Tang and Bo Xu. Automatic Recovery of Z-Jumps for Neuronal Morphology Reconstruction. Frontiers in Neuroinformatics. Conference Abstract: The 7th Neuroinformatics Congress (Neuroinformatics 2014), Leiden, the Netherlands, August 25-27, 2014.

• Yi Zeng, Weida Bi, Xuan Tang, and Bo Xu. Neuronal Morphology Modeling Based on Microscopy Reconstruction Data in the Public Repositories. Proceedings of the 2014 International Conference on Brain Informatics and Health (BIH 2014), Lecture Notes in Artificial Intelligence 8609, 1-11, Springer, Warsaw, Poland, August 11-14, 2014.

* Brain-inspired Artificial Intelligence: Brain-inspired Neural Networks, Brain-inspired Cognitive Robotics
* Ethics and Governance of Artificial Intelligence