傅沁冰副教授
一、基本信息
1.傅沁冰博士，副教授，硕士生导师
2.研究领域： 计算神经科学、神经网络、运动感知、机器人
3.办公地点： 电子信息实验楼610室
4.办公电话：+8613518187096
5.电子邮箱：qifu@gzhu.edu.cn
二、个人简介
傅沁冰于2018年10月被英国林肯大学授予博士学位，2019年-2021年工作于广州大学（博士后），2021年12月正式入职广州大学数学与信息科学学院。共发表学术论文60余篇，其中包括IEEE Transactions期刊、Neural Networks等知名期刊。研究领域主要围绕类脑智能中的运动感知与机器视觉应用，是多个顶级期刊的评审，担任SCI期刊的客座主编。
三、教育背景
2014-2018 英国林肯大学 博士学位
2011-2014 英国林肯大学 硕士学位
2005-2009 电子科技大学 学士学位
四、职业经历
1.学术工作经历
2024-至今 广州大学数学与信息科学学院 副教授
2021-2024 广州大学数学与信息科学学院 讲师
2019-2021 广州大学 博士后
2. 海外工作经历
无
五、教授课程
信息科学类课程
六、科研服务
2023-2027 国家自然科学基金面上项目 主持
2022-2025 教育部社科司项目 主持
2020-至今 国家自然科学基金重点项目 参与
七、研究成果
近5年期刊论著目录
[1] Z. Dai, Q. Fu, J. Peng, H. Li, “SloN: a spiking looming perception network exploiting neural encoding and processing in ON/OFF channels”, Frontiers in Neuroscience, 2024.
[2] Z. Qin, Q. Fu, J. Peng, “A computationally efficient and robust looming perception model based on dynamic neural field”, Neural Networks, 2024.
[3] Z. Chang, Q. Fu, M. Hua, J. Peng, “Feedback neural computation in collision perception: Towards diverse selectivity”, Neurocomputing, 2024.
[4] J. Hong, X. Sun, J. Peng, Q. Fu, “A Bio-Inspired Probabilistic Neural Network Model for Noise-Resistant Collision Perception”, Biomimetics, 2024.
[5] X. Sun, C. Hu, T. Liu, S. Yue, J. Peng, Q. Fu, “Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics”, Biomimetics, 2023.
[6] Q. Fu, “Motion perception based on ON/OFF channels: A survey”, Neural Networks, 2023.
[7] X. Sun, Q. Fu, J. Peng, S. Yue, “An insect-inspired model facilitating autonomous navigation by incorporating goal approaching and collision avoidance”, Neural Networks, 2023.
[8] Z. Chang, Q. Fu, H. Chen, H. Li, J. Peng, “A look into feedback neural computation upon collision selectivity”, Neural Networks, 2023
[9] H. Luan, M. Hua, Y. Zhang, S. Yue, Q. Fu, “A BLG1 neural model implements the unique looming selectivity to diving target”, Optoelectronics Letters, 2023.
[10] Q. Fu, Z. Li, J. Peng, “Harmonizing motion and contrast vision for robust looming detection”, Array, 2022.
[11] H. Luan, Q. Fu, Y. Zhang, M. Hua, S. Chen, S. Yue, “A looming spatial localization neural network inspired by MLG1 neurons in the crab Neohelice”, Frontiers in Neuroscience, 2022.
[12] T. Liu, X. Sun, C. Hu, Q. Fu, S. Yue, “A multiple pheromone communication system for swarm intelligence”, IEEE Access, 2021.
[13] Q. Fu, X. Sun, T. Liu, C. Hu, S. Yue, “Robustness of Bio-Inspired Visual Systems for Collision Prediction in Critical Robot Traffic”, Frontiers in Robotics and AI, 2021.
[14] H. Isakhani, N. Bellotto, Q. Fu, S. Yue, “Generative design and fabrication of a locust-inspired gliding wing prototype for micro aerial robots”, Journal of Computational Design and Engineering, 2021.
[15] H. Wang, Q. Fu, H. Wang, P. Baxter, J. Peng, S. Yue, “A bioinspired angular velocity decoding neural network model for visually guided flights”, Neural Networks, 2021.
[16] Q. Fu, H. Wang, J. Peng, S. Yue, “Improved collision perception neuronal system model with adaptive inhibition mechanism and evolutionary learning”, IEEE Access, 2020.
[17] Q. Fu, S. Yue, “Modelling Drosophila motion vision pathways for decoding the direction of translating objects against cluttered moving backgrounds”, Biological Cybernetics, 2020.
[18] Q. Fu, C. Hu, J. Peng, F. C. Rind, S. Yue, “A robust collision perception visual neural network with specific selectivity to darker objects”, IEEE Transactions on Cybernetics, 2020.
[19] Q. Fu, H. Wang, C. Hu, S. Yue, “Towards computational models and applications of insect visual systems for motion perception: A review”, Artificial Life, 2019.
[20] Q. Fu, C. Hu, J. Peng, S. Yue, “Shaping the collision selectivity in a looming sensitive neuron model with parallel ON and OFF pathways and spike frequency adaptation”, Neural Networks, 2018.
