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.

Preprints

  1. Qiang Yu(*), Shiming Song, Chenxiang Ma, Linqiang Pan and Kay Chen Tan,
    “Synaptic Learning with Augmented Spikes”,
    arXive:2005.04820.

  2. Qiang Yu(*), Chenxiang Ma, Shiming Song, Gaoyan Zhang, Jianwu Dang and Kay Chen Tan,
    “Constructing Accurate and Efficient Deep Spiking Neural Networks with Double-threshold and Augmented Schemes”,
    arXive:2005.03231.

  3. Shiming Song, Qiang Yu(*), et. al. ,
    “Efficient Learning with Augmented Spikes: A Case Study with Image Classification”.

Books

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

Journal Papers

  1. Tingfang Wu, Linqiang Pan, Qiang Yu and Kay Chen Tan,
    “Numerical Spiking Neural P Systems”,
    IEEE Trans. On Neural Networks and Learning Systems, in press, 2020. [IF:8.793, SCI-1]

  2. Qiang Yu(*), Shenglan Li, Huajin Tang, Longbiao Wang, Jianwu Dang and Kay Chen Tan,
    “Towards efficient processing and learning with spikes: new approaches for multi-spike learning”,
    IEEE Trans. On Cybernetics, in press, 2020. [IF:11.079, SCI-1]

  3. Qiang Yu(*), Yanli Yao, Longbiao Wang(*), Huajin Tang, Jianwu Dang and Kay Chen Tan,
    “Robust environmental sound recognition with sparse key-point encoding and efficient multi-spike learning”,
    IEEE Trans. On Neural Networks and Learning Systems, in press, 2020. [IF:8.793, SCI-1]

  4. Qiang Yu(*), Haizhou Li and Kay Chen Tan,
    “Spike Timing or Rate? Neurons Learn to Make Decisions for Both Through Threshold-Driven Plasticity”,
    IEEE Trans. On Cybernetics, 49(6): 2178-2189, 2019. [IF:11.079, SCI-1]

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

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

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

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

Conference Papers (Selected)

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

  2. 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)

  3. 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)

  4. 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)

  5. 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)