224 citations · 570 across the 28 of their papers we have counts for
28 papers
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Wei Fang, Yanqi Chen, Jianhao Ding +7
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As…
Unsupervised Optical Flow Estimation with Dynamic Timing Representation for Spike Camera
Lujie Xia, Ziluo Ding, Rui Zhao +5
Efficiently selecting an appropriate spike stream data length to extract precise information is the key to the spike vision tasks. To address this issue, we propose a dynamic timin…
Unveiling the Potential of Spike Streams for Foreground Occlusion Removal from Densely Continuous Views
Jiyuan Zhang, Shiyan Chen, Yajing Zheng +2
The extraction of a clean background image by removing foreground occlusion holds immense practical significance, but it also presents several challenges. Presently, the majority o…
Spike timing reshapes robustness against attacks in spiking neural networks
Jianhao Ding, Zhaofei Yu, Tiejun Huang +1
The success of deep learning in the past decade is partially shrouded in the shadow of adversarial attacks. In contrast, the brain is far more robust at complex cognitive tasks. Ut…
One Forward is Enough for Neural Network Training via Likelihood Ratio Method
Jinyang Jiang, Zeliang Zhang, Chenliang Xu +2
While backpropagation (BP) is the mainstream approach for gradient computation in neural network training, its heavy reliance on the chain rule of differentiation constrains the de…
Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
Wei Fang, Zhaofei Yu, Zhaokun Zhou +5
Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fire-reset neuronal dynamics, which can only be simulated serially and can hardly learn long-time dependencies.…
Spike Stream Denoising via Spike Camera Simulation
Liwen hu, Lei Ma, Zhaofei Yu +2
As a neuromorphic sensor with high temporal resolution, the spike camera shows enormous potential in high-speed visual tasks. However, the high-speed sampling of light propagation…
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
Tong Bu, Wei Fang, Jianhao Ding +3
Spiking Neural Networks (SNNs) have gained great attraction due to their distinctive properties of low power consumption and fast inference on neuromorphic hardware. As the most ef…
Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios
Shiyan Chen, Jiyuan Zhang, Zhaofei Yu +1
Self-supervised denoising has attracted widespread attention due to its ability to train without clean images. However, noise in real-world scenarios is often spatially correlated,…
SpikeCV: Open a Continuous Computer Vision Era
Yajing Zheng, Jiyuan Zhang, Rui Zhao +5
SpikeCV is a new open-source computer vision platform for the spike camera, which is a neuromorphic visual sensor that has developed rapidly in recent years. In the spike camera, e…
A Unified Framework for Soft Threshold Pruning
Yanqi Chen, Zhengyu Ma, Wei Fang +3
Soft threshold pruning is among the cutting-edge pruning methods with state-of-the-art performance. However, previous methods either perform aimless searching on the threshold sche…
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes
Zecheng Hao, Jianhao Ding, Tong Bu +2
Spiking Neural Networks (SNNs) have attracted great attention due to their distinctive characteristics of low power consumption and temporal information processing. ANN-SNN convers…
Reducing ANN-SNN Conversion Error through Residual Membrane Potential
Zecheng Hao, Tong Bu, Jianhao Ding +2
Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips. Amon…
A Novel Noise Injection-based Training Scheme for Better Model Robustness
Zeliang Zhang, Jinyang Jiang, Minjie Chen +3
Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work. In this work, we propose a novel noise injection-ba…
Optimized Potential Initialization for Low-latency Spiking Neural Networks
Tong Bu, Jianhao Ding, Zhaofei Yu +1
Spiking Neural Networks (SNNs) have been attached great importance due to the distinctive properties of low power consumption, biological plausibility, and adversarial robustness.…
1000x Faster Camera and Machine Vision with Ordinary Devices
Tiejun Huang, Yajing Zheng, Zhaofei Yu +13
In digital cameras, we find a major limitation: the image and video form inherited from a film camera obstructs it from capturing the rapidly changing photonic world. Here, we pres…
Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation
Ziluo Ding, Rui Zhao, Jiyuan Zhang +4
Event camera has offered promising alternative for visual perception, especially in high speed and high dynamic range scenes. Recently, many deep learning methods have shown great…
Pruning of Deep Spiking Neural Networks through Gradient Rewiring
Yanqi Chen, Zhaofei Yu, Wei Fang +2
Spiking Neural Networks (SNNs) have been attached great importance due to their biological plausibility and high energy-efficiency on neuromorphic chips. As these chips are usually…
Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks
Jianhao Ding, Zhaofei Yu, Yonghong Tian +1
Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have attracted great attentions from researchers and industry. The most efficient way to train dee…
Deep Residual Learning in Spiking Neural Networks
Wei Fang, Zhaofei Yu, Yanqi Chen +3
Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics. Conside…
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
Wei Fang, Zhaofei Yu, Yanqi Chen +3
Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal information processing capability, low power consumption, and high biological plausibility.…
Towards the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes
Zhaofei Yu, Jian K. Liu, Shanshan Jia +4
Neuroprosthesis, as one type of precision medicine device, is aiming for manipulating neuronal signals of the brain in a closed-loop fashion, together with receiving stimulus from…
Reconstruction of Natural Visual Scenes from Neural Spikes with Deep Neural Networks
Yichen Zhang, Shanshan Jia, Yajing Zheng +5
Neural coding is one of the central questions in systems neuroscience for understanding how the brain processes stimulus from the environment, moreover, it is also a cornerstone fo…
Probabilistic Inference of Binary Markov Random Fields in Spiking Neural Networks through Mean-field Approximation
Yajing Zheng, Shanshan Jia, Zhaofei Yu +3
Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like…