activity
20172022
most citedSpatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks

1.2k citations · 1.5k across the 10 of their papers we have counts for

collaborators

10 papers

cs.NE2022★ 6 cited

Advancing Deep Residual Learning by Solving the Crux of Degradation in Spiking Neural Networks

Yifan Hu, Yujie Wu, Lei Deng +1

Despite the rapid progress of neuromorphic computing, the inadequate depth and the resulting insufficient representation power of spiking neural networks (SNNs) severely restrict t…

cs.NE2020★ 11 cited

Going Deeper With Directly-Trained Larger Spiking Neural Networks

Hanle Zheng, Yujie Wu, Lei Deng +2

Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal information and event-driven signal processing, which is very suited for energy-efficient…

cs.NE2020★ 1 cited

Brain-inspired global-local learning incorporated with neuromorphic computing

Yujie Wu, Rong Zhao, Jun Zhu +11

Two main routes of learning methods exist at present including error-driven global learning and neuroscience-oriented local learning. Integrating them into one network may provide…

cs.CV2020★ 15 cited

Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences

Weihua He, YuJie Wu, Lei Deng +6

Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion. Sp…

cs.NE2020★ 16 cited

Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient

Ling Liang, Xing Hu, Lei Deng +5

Recently, backpropagation through time inspired learning algorithms are widely introduced into SNNs to improve the performance, which brings the possibility to attack the models ac…

cs.NE2019★ 13 cited

Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization

Lei Deng, Yujie Wu, Yifan Hu +6

As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with h…

cs.CV2019★ 27 cited

DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking

Zheyu Yang, Yujie Wu, Guanrui Wang +5

Computer-science-oriented artificial neural networks (ANNs) have achieved tremendous success in a variety of scenarios via powerful feature extraction and high-precision data opera…

physics.app-ph2018★ 126 cited

Truly concomitant and independently expressed short- and long-term plasticity in Bi2O2Se-based three-terminal memristor

Ziyang Zhang, Tianran Li, Yujie Wu +10

Orchestration of diverse synaptic plasticity mechanisms across different timescales produces complex cognitive processes. To achieve comparable cognitive complexity in memristive n…

cs.NE2018★ 102 cited

Direct Training for Spiking Neural Networks: Faster, Larger, Better

Yujie Wu, Lei Deng, Guoqi Li +2

Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown competitive p…

cs.NE2017★ 1.2k cited

Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks

Yujie Wu, Lei Deng, Guoqi Li +2

Compared with artificial neural networks (ANNs), spiking neural networks (SNNs) are promising to explore the brain-like behaviors since the spikes could encode more spatio-temporal…