Qiang YU
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Publications

Notes: Selected publications are presented here, and a more complete list can be found at Google Scholar.

  • Papers are listed by year of acceptance or publication.

  • (*) denotes corresponding author.

Books

  1. "Neuromorphic Cognitive Systems: A Learning and Memory Centered Approach",
    Qiang Yu, H. Tang, J. Hu and K. C. Tan,
    Intelligent Systems Reference Library Series, Springer, May 2017.

Journal Papers

  1. "Improving Multispike Learning With Plastic Synaptic Delays",
    Qiang Yu(*), Jialu Gao, Jianguo Wei, Jing Li, Kay Chen Tan and Tiejun Huang,
    IEEE Trans. On Neural Networks and Learning Systems, 34(12):10254-10265, 2023. [IF:10.451, SCI-Q1] [Link]

  2. "Deep Spike Learning with Local Classifiers",
    Chenxiang Ma, Rui Yan, Zhaofei Yu and Qiang Yu(*),
    IEEE Trans. On Cybernetics, 53(5):3363-3375, 2023. [IF:11.448, SCI-Q1] [Link]

  3. "联合突触权重和延迟可塑性的高效多脉冲学习算法研究",
    Jialu Gao, Qiang Yu(*), Huajin Tang and Tiejun Huang,
    计算机学报, 45(10):2065-2079, 2022. [CCF-A]

  4. "Efficient Learning with Augmented Spikes: A Case Study with Image Classification",
    Shiming Song, Chenxiang Ma, Wei Sun, Junhai Xu, Jianwu Dang and Qiang Yu(*),
    Neural Networks, 142:205-212, 2021. [IF:8.050, SCI-Q1] [Link]

  5. "Temporal Encoding and Multi-spike Learning Framework for Efficient Recognition of Visual Patterns",
    Qiang Yu(*), Shiming Song, Chenxiang Ma, Jianguo Wei(*), Shengyong Chen and Kay Chen Tan,
    IEEE Trans. On Neural Networks and Learning Systems, 33(8):3387-3399, 2022. [IF:10.451, SCI-Q1] [Link]

  6. "Synaptic Learning with Augmented Spikes",
    Qiang Yu(*), Shiming Song, Chenxiang Ma, Linqiang Pan and Kay Chen Tan,
    IEEE Trans. On Neural Networks and Learning Systems, 33(3):1134-1146, 2022. [IF:10.451, SCI-Q1] [Link]

  7. "Constructing Accurate and Efficient Deep Spiking Neural Networks with Double-threshold and Augmented Schemes",
    Qiang Yu(*), Chenxiang Ma, Shiming Song, Gaoyan Zhang, Jianwu Dang and Kay Chen Tan,
    IEEE Trans. On Neural Networks and Learning Systems, 33(4): 1714-1726, 2022. [IF:10.451, SCI-Q1] [Link]

  8. "Numerical Spiking Neural P Systems",
    Tingfang Wu, Linqiang Pan, Qiang Yu and Kay Chen Tan,
    IEEE Trans. On Neural Networks and Learning Systems, 32(6):2443-2457, 2021. [IF:10.451, SCI-Q1] [Link]

  9. "Toward efficient processing and learning with spikes: new approaches for multi-spike learning",
    Qiang Yu(*), Shenglan Li, Huajin Tang, Longbiao Wang, Jianwu Dang and Kay Chen Tan,
    IEEE Trans. On Cybernetics, 52(3):1364-1376, 2022. [IF:11.448, SCI-Q1] [Link]

  10. "Robust environmental sound recognition with sparse key-point encoding and efficient multi-spike learning",
    Qiang Yu(*), Yanli Yao, Longbiao Wang(*), Huajin Tang, Jianwu Dang and Kay Chen Tan,
    IEEE Trans. On Neural Networks and Learning Systems, 32(2):625-638, 2021. [IF:8.793, SCI-Q1] [Link]

  11. "Spike Timing or Rate? Neurons Learn to Make Decisions for Both Through Threshold-Driven Plasticity",
    Qiang Yu(*), Haizhou Li and Kay Chen Tan,
    IEEE Trans. On Cybernetics, 49(6): 2178-2189, 2019. [IF:11.079, SCI-Q1] [Link]

  12. "A Spiking Neural Network System for Robust Sequence Recognition",
    Qiang Yu, R. Yan, H. Tang, K. C. Tan, and H. Li,
    IEEE Trans. On Neural Networks and Learning Systems, 27(3):621-635, 2016.

  13. "A brain-inspired spiking neural network model with temporal encoding and learning",
    Qiang Yu, H. Tang, K. C. Tan, and H. Yu,
    Neurocomputing, 138:3-13, 2014.

  14. "Rapid feedforward computation by temporal encoding and learning with spiking neurons",
    Qiang Yu, H. Tang, K. C. Tan, and H. Li,
    IEEE Trans. On Neural Networks and Learning Systems, 24(10): 1539-1552, 2013.

  15. "Precise-spike-driven synaptic plasticity: learning hetero-association of spatiotemporal spike patterns",
    Qiang Yu, H. Tang, K. C. Tan, and H. Li,
    PLoS One, 8(11): e78318, 2013.

Conference Papers (After 2019)

  1. Chenxiang Ma, Junhai Xu and Qiang Yu(*), “Temporal Dependent Local Learning for Deep Spiking Neural Networks”, in IJCNN, Shenzhen China, Jul 2021. (CCF-C)

  2. Chenxiang Ma, Junhai Xu and Qiang Yu(*), “A Deep Spike Learning through Critical Time Points”, in IJCNN, Shenzhen China, Jul 2021. (CCF-C)

  3. Chenxiang Ma and Qiang Yu(*), “AugMapping: Accurate and Efficient Inference with Deep Double-Threshold Spiking Neural Networks”, in SSCI, Virtual, 2020. (EI)

  4. Shiming Song and Qiang Yu(*), “Brain-Inspired Framework for Image Classification with A New Unsupervised Matching Pursuit Encoding”, in ICONIP, Thailand, 2020. (CCF-C)

  5. Shenglan Li and Qiang Yu(*), “New Efficient Multi-Spike Learning for Fast Processing and Robust Learning”, in AAAI, New York, USA, 2020. (CCF-A)

  6. Rong Xiao, Qiang Yu, Rui Yan and Huajin Tang, “Fast and accurate classification with a multi-spike learning algorithm for spiking neurons”, in IJCAI, Macao, China, 2019. (CCF-A)

  7. Yanli Yao, Qiang Yu(*), Longbiao Wang and Jianwu Dang, “Robust sound event classification with local time-frequency information and convolutional neural networks”, in ICANN, Munich, Germany, 2019. (CCF-C)

  8. Yanli Yao, Qiang Yu(*), Longbiao Wang and Jianwu Dang, “A spiking neural network with distributed keypoint encoding for robust sound recognition”, in IJCNN, Budapest, Hungary, 2019. (CCF-C)

  9. Qiang Yu(*), Yanli Yao, Longbiao Wang, Huajin Tang and Jianwu Dang, “A multi-spike approach for robust sound recognition”, in ICASSP, Brighton, UK, 2019. (CCF-B)

  10. Yanli Yao, Qiang Yu(*), Longbiao Wang and Jianwu Dang, “An integrated system for robust gender classification with convolutional restricted Boltzmann machine and spiking neural network”, in SSCI, Xiamen, China, 2019. (EI)

  11. Shiming Song, Qiang Yu(*), Longbiao Wang and Jianwu Dang, “A Matching Pursuit Approach for Image Classification with Spiking Neural Networks”, in SSCI, Xiamen, China, 2019. (EI)