徐明坤
博士，青年研究员，研究组组长
Email: xumingkun@@gdiist.cn
个人简介：
徐明坤，青年研究员，类脑智能算法与模型研究组组长。2018年本科毕业于西安电子科技大学，2023年于清华大学获得博士学位。2023年—2025年期间在广东省智能科学与技术研究院从事博士后研究。徐明坤博士专注于类脑算法、人工智能模型与BI for Science智能应用，相关研究工作在Nature Communications、Science Robotics、IEEE Transactions on Neural Networks and Learning Systems等高水平期刊和ICML、AAAI、CVPR、IJCAI、EMNLP等人工智能顶会上发表论文30余篇，并已获得多项国家发明专利授权。
类脑智能算法与模型研究组
本研究组专注于探索“人工智能 × 大模型 × 脑科学”的交叉前沿，致力于构建兼具生物可解释性与通用智能能力的新一代人工智能体系。以神经科学发现为指导，以机器学习与高性能计算为手段，从神经元尺度到系统级智能横向贯通，自底向上打造具备推理、记忆、持续学习与自主适应的类脑智能框架。通过跨学科协同，力求揭示类脑计算的核心机理，并将其转化为服务于机器人、自动驾驶、生命健康等领域的关键技术与创新应用。
当前，研究组的核心研究方向包括：
1. 类脑智能算法
依托脉冲神经网络的时空特性，面向记忆巩固、连续学习、树突非线性整合、稳态机制、Binding 机制与认知地图等关键脑启发原理，设计低能耗、可扩展的学习规则与计算模型。研究重点涵盖突触可塑性、表征机制、元学习驱动的任务快速迁移、以及动态结构重塑等机制，力求在跨模态、多任务环境中构建具备“可解释-可迁移-可增量演化”三维能力的类脑智能体，为智能应用与大模型融合奠定生物学与工程并重的算法基础。
2. 深度学习与大模型
重点围绕基础模型架构设计、参数微调与快速推理三条主线展开。架构层面，以 Transformer／Mamba 等代表网络为原型，探索从视觉到跨模态的创新高效架构及统一大模型框架；在训练层面，结合 LoRA 等轻量策略与检索增强方法，实现低成本微调与增量学习；在推理层面，聚焦 KV-Cache 优化、低比特量化等技术，构建低时延、低功耗的端-云协同推理体系。此外，将探索生成模型、图表示学习、强化学习驱动的 Agent 架构等相关技术，赋予模型感知-记忆-决策的闭环能力，为类脑应用提供坚实算法底座。
3. 类脑智能应用与BI for Science交叉赋能
将前述算法模型应用到自动驾驶、机器人协作与终端设备在线学习等复杂场景，打造从感知—决策—执行的全链路解决方案，显著提升智能体在动态不确定环境中的自主适应与能效比。此外，融合类脑算法与大模型技术，面向医疗诊断、核酸分子检测、蛋白质属性预测等问题，构建任务驱动的智能分析工具，为生命健康、材料研发等领域提供突破性方案。
代表论著：
1. Xu M, Liu F, Hu Y, Li H, Wei Y, Zhong S, Pei J* and Deng L*. Adaptive Synaptic Scaling in Spiking Networks for Continual Learning and Enhanced Robustness[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024. (中科院一区SCI，TOP期刊)
2. Xu M, Wu Y, Deng L, Liu F, Li G and Pei J*. Exploiting spiking dynamics with spatial-temporal feature normalization in graph learning. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), pages 3207-3213, 2021. (人工智能国际会议，CCF A类)
3. Yang Y#, Xu M#（共同一作）, Jia S, Wang B, Xu L, Wang X, Liu H, Liu Y, Guo Y, Wang L, Duan S, Liu K, Zhu M, Pei J, Duan W, Liu D, Li H*. A new opportunity for the emerging tellurium semiconductor: making resistive switching devices[J]. Nature Communications, 2021, 12(1): 1-12. (中科院一区SCI，TOP期刊)
4. Wu Y#, Zhao R#, Zhu J#, Chen F#, Xu M#（共同一作）, Li G, Song S, Deng L, Wang G, Zheng H, Ma S, Pei J, Zhang Y, Zhao M, Shi L*. Brain-inspired global-local learning incorporated with neuromorphic computing[J]. Nature Communications, 2022, 13(1): 1-14. (中科院一区SCI， TOP期刊)
5. Xu M*. Exploiting homeostatic synaptic modulation in spiking neural networks for semi-supervised graph learning[C]. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management(CIKM). 5193-5195. (2023) (人工智能国际会议，CCF B类)
6. Chen Y, Song A, Yin H, Zhong S, Chen F, Xu Q, Wang S*, Xu M*（通讯作者）. Multi-view incremental learning with structured hebbian plasticity for enhanced fusion efficiency[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2025, 39(2): 1265-1273. (人工智能国际会议，CCF A类)
7. Wang Y, Fang X, Yin H, Li D, Li G, Xu Q*, Xu Y, Zhong S, Xu M*（通讯作者）. BIG-FUSION: Brain-Inspired Global-Local Context Fusion Framework for Multimodal Emotion Recognition in Conversations[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2025, 39(2): 1574-1582. (人工智能国际会议，CCF A类)
8. Wang Y, Tan S, Shen J, Xu Y, Song H, Xu Q, Tiwari P, Xu M*（通讯作者）. Enhancing Graph Contrastive Learning for Protein Graphs from Perspective of Invariance[C]// Forty-Second International Conference on Machine Learning (ICML). (Conference Track). 2025. (人工智能国际会议，CCF A类)
9. Xu M, Zheng H, Pei J, Deng L*. A Unified Structured Framework for AGI: Bridging Cognition and Neuromorphic Computing [C]//Artificial General Intelligence: 16th International Conference, AGI 2023, Stockholm, Sweden, June 16–19, 2023, Proceedings. Cham: Springer Nature Switzerland, 2023: 345-356.
10. Wang Y, Li D, Shen J, Xu Y, Zhong S, Xu M*（通讯作者）. ClingTP: Curriculum Learning based Multi-style Title Prefix Generation[C]//ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025: 1-5. (人工智能国际会议，CCF B类)
11. Xu M, Liu F, Pei J*. Endowing spiking neural networks with homeostatic adaptivity for APS-DVS bimodal scenarios. [C]//Companion Publication of the 2022 International Conference on Multimodal Interaction(ICMI). 2022: 12-17.
12. Ran X#, Xu M#（共同一作）, Mei L, Xu Q, Liu Q*. Detecting out-of-distribution samples via variational auto-encoder with reliable uncertainty estimation[J]. Neural Networks, 2022, 145: 199-208.
13. Liu F#, Xu M#（共同一作）, Li G, Pei J, Shi L, Zhao R*. Adversarial symmetric GANs: Bridging adversarial samples and adversarial networks[J]. Neural Networks, 2021, 133: 148-156.
14. Wang Y#, Zhang Z#, Xu M#（共同一作）, Yang Y, Ma M, Li H, Pei J, Shi L*. Self-doping memristors with equivalently synaptic ion dynamics for neuromorphic computing[J]. ACS applied materials & interfaces, 2019, 11(27): 24230-24240. (中科院一区SCI，TOP期刊，封面文章)
15. Song A, Chen Y, Wang Y, Zhong S, Xu M*(通讯作者). Orchestrating Plasticity and Stability: A Continual Knowledge Graph Embedding Framework with Bio-Inspired Dual-Mask Mechanism[C]//The 16th Asian Conference on Machine Learning (Conference Track). 2024.
16. Zhong S*, Su L, Xu M, Loke D, Yu B, Zhang Y, Zhao R*. Recent Advances in Artificial Sensory Neurons: Biological Fundamentals, Devices, Applications, and Challenges[J]. Nano-Micro Letters, 2025, 17(1): 61. (中科院一区SCI，TOP期刊)
17. Liu Z, Chen J, Xu M, Ho S, Wei Y*, Ho HP, Yong KT*. Engineered multi-domain lipid nanoparticles for targeted delivery[J]. Chemical Society Reviews, 2025. (中科院一区SCI，TOP期刊)
18. Yu F, Wu Y, Ma S, Xu M, Li H, Qu H, Song C, Wang T, Zhao R, Shi L*. NeuroGPR: Brain-inspired General Place Recognition with Neuromorphic Computing [J]. Science Robotics, 2023, 8(78): eabm6996. (中科院一区SCI，TOP期刊)
19. Zhao R, Yang Z, Zheng H, Wu Y, Liu F, Wu Z, Li L, Chen F, Song S, Zhu J, Zhang W, Huang H, Xu M, Sheng K, Yin Q, Pei J, Li G, Zhang Y, Zhao M, Shi L*. A framework for the general design and computation of hybrid neural networks[J]. Nature Communications, 2022, 13(1): 1-12. (中科院一区SCI，TOP期刊)
