| Precise-spike-driven synaptic plasticity: Learning hetero-association of spatiotemporal spike patterns Q Yu, H Tang, KC Tan, H Li Plos one 8 (11), e78318, 2013 | 192 | 2013 |
| Rapid feedforward computation by temporal encoding and learning with spiking neurons Q Yu, H Tang, KC Tan, H Li IEEE transactions on neural networks and learning systems 24 (10), 1539-1552, 2013 | 164 | 2013 |
| A brain-inspired spiking neural network model with temporal encoding and learning Q Yu, H Tang, KC Tan, H Yu Neurocomputing 138, 3-13, 2014 | 140 | 2014 |
| A spiking neural network system for robust sequence recognition Q Yu, R Yan, H Tang, KC Tan, H Li IEEE Transactions on Neural Networks and Learning Systems 27 (3), 621 - 635, 2016 | 92 | 2016 |
| Spike timing or rate? Neurons learn to make decisions for both through threshold-driven plasticity Q Yu, H Li, KC Tan IEEE transactions on cybernetics 49 (6), 2178-2189, 2019 | 66 | 2019 |
| Temporal coding of local spectrogram features for robust sound recognition J Dennis, Q Yu, H Tang, HD Tran, H Li 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 66 | 2013 |
| Numerical spiking neural P systems T Wu, L Pan, Q Yu, KC Tan IEEE Transactions on Neural Networks and Learning Systems 32 (6), 2443-2457, 2020 | 57 | 2020 |
| Constructing accurate and efficient deep spiking neural networks with double-threshold and augmented schemes Q Yu, C Ma, S Song, G Zhang, J Dang, KC Tan IEEE Transactions on Neural Networks and Learning Systems 33 (4), 1714-1726, 2021 | 45 | 2021 |
| Robust environmental sound recognition with sparse key-point encoding and efficient multi-spike learning Q Yu, Y Yao, L Wang, H Tang, J Dang, KC Tan IEEE Transactions on Neural Networks and Learning Systems, 2020 | 24 | 2020 |
| Pattern recognition computation in a spiking neural network with temporal encoding and learning Q Yu, KC Tan, H Tang The 2012 International Joint Conference on Neural Networks (IJCNN), 1-7, 2012 | 22 | 2012 |
| Gender-aware CNN-BLSTM for speech emotion recognition L Zhang, L Wang, J Dang, L Guo, Q Yu Artificial Neural Networks and Machine Learning–ICANN 2018: 27th …, 2018 | 20 | 2018 |
| Neuromorphic cognitive systems Q Yu, H Tang, J Hu, KC Tan A Learning and Memory Centered Approach, 2017 | 20 | 2017 |
| Fast and accurate classification with a multi-spike learning algorithm for spiking neurons. R Xiao, Q Yu, R Yan, H Tang IJCAI, 1445-1451, 2019 | 17 | 2019 |
| Toward efficient processing and learning with spikes: New approaches for multispike learning Q Yu, S Li, H Tang, L Wang, J Dang, KC Tan IEEE transactions on cybernetics 52 (3), 1364-1376, 2020 | 16 | 2020 |
| Deep spike learning with local classifiers C Ma, R Yan, Z Yu, Q Yu IEEE Transactions on Cybernetics, 2022 | 12 | 2022 |
| Temporal dependent local learning for deep spiking neural networks C Ma, J Xu, Q Yu 2021 International Joint Conference on Neural Networks (IJCNN), 1-7, 2021 | 11 | 2021 |
| Synaptic learning with augmented spikes Q Yu, S Song, C Ma, L Pan, KC Tan IEEE Transactions on Neural Networks and Learning Systems 33 (3), 1134-1146, 2021 | 11 | 2021 |
| Temporal encoding and multispike learning framework for efficient recognition of visual patterns Q Yu, S Song, C Ma, J Wei, S Chen, KC Tan IEEE Transactions on Neural Networks and Learning Systems 33 (8), 3387-3399, 2021 | 10 | 2021 |
| Learning real-world stimuli by single-spike coding and tempotron rule H Tang, Q Yu, KC Tan The 2012 International Joint Conference on Neural Networks (IJCNN), 1-6, 2012 | 10 | 2012 |
| Associative memory model of hippocampus CA3 using spike response neurons CH Tan, EY Cheu, J Hu, Q Yu, H Tang Neural Information Processing: 18th International Conference, ICONIP 2011 …, 2011 | 10 | 2011 |
