87 citations · 236 across the 20 of their papers we have counts for
20 papers
Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistency
Dongcheng Zhao, Guobin Shen, Yiting Dong +2
Spiking neural networks have gained significant attention due to their brain-like information processing capabilities. The use of surrogate gradients has made it possible to train…
Dive into the Power of Neuronal Heterogeneity
Guobin Shen, Dongcheng Zhao, Yiting Dong +2
The biological neural network is a vast and diverse structure with high neural heterogeneity. Conventional Artificial Neural Networks (ANNs) primarily focus on modifying the weight…
Temporal Knowledge Sharing enable Spiking Neural Network Learning from Past and Future
Yiting Dong, Dongcheng Zhao, Yi Zeng
Spiking Neural Networks (SNNs) have attracted significant attention from researchers across various domains due to their brain-like information processing mechanism. However, SNNs…
MSAT: Biologically Inspired Multi-Stage Adaptive Threshold for Conversion of Spiking Neural Networks
Xiang He, Yang Li, Dongcheng Zhao +2
Spiking Neural Networks (SNNs) can do inference with low power consumption due to their spike sparsity. ANN-SNN conversion is an efficient way to achieve deep SNNs by converting we…
An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain
Xiang He, Dongcheng Zhao, Yang Li +3
Spiking neural networks (SNNs) are rich in spatio-temporal dynamics and are suitable for processing event-based neuromorphic data. However, event-based datasets are usually less an…
Exploiting High Performance Spiking Neural Networks with Efficient Spiking Patterns
Guobin Shen, Dongcheng Zhao, Yi Zeng
Spiking Neural Networks (SNNs) use discrete spike sequences to transmit information, which significantly mimics the information transmission of the brain. Although this binarized f…
Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoning
Hongjian Fang, Yi Zeng, Jianbo Tang +3
How neural networks in the human brain represent commonsense knowledge, and complete related reasoning tasks is an important research topic in neuroscience, cognitive science, psyc…
Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation
Yang Li, Xiang He, Yiting Dong +2
Spiking neural network (SNN) has been attached to great importance due to the properties of high biological plausibility and low energy consumption on neuromorphic hardware. As an…
BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation
Yi Zeng, Dongcheng Zhao, Feifei Zhao +16
Spiking neural networks (SNNs) have attracted extensive attentions in Brain-inspired Artificial Intelligence and computational neuroscience. They can be used to simulate biological…
An Unsupervised STDP-based Spiking Neural Network Inspired By Biologically Plausible Learning Rules and Connections
Yiting Dong, Dongcheng Zhao, Yang Li +1
The backpropagation algorithm has promoted the rapid development of deep learning, but it relies on a large amount of labeled data and still has a large gap with how humans learn.…
Solving the Spike Feature Information Vanishing Problem in Spiking Deep Q Network with Potential Based Normalization
Yinqian Sun, Yi Zeng, Yang Li
Brain inspired spiking neural networks (SNNs) have been successfully applied to many pattern recognition domains. The SNNs based deep structure have achieved considerable results i…
Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes
Yang Li, Yi Zeng
Spiking neural network (SNN), as a brain-inspired energy-efficient neural network, has attracted the interest of researchers. While the training of spiking neural networks is still…
Spiking CapsNet: A Spiking Neural Network With A Biologically Plausible Routing Rule Between Capsules
Dongcheng Zhao, Yang Li, Yi Zeng +2
Spiking neural network (SNN) has attracted much attention due to their powerful spatio-temporal information representation ability. Capsule Neural Network (CapsNet) does well in as…
Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networks
Guobin Shen, Dongcheng Zhao, Yi Zeng
The spiking neural network (SNN) mimics the information processing operation in the human brain, represents and transmits information in spike trains containing wealthy spatial and…
BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons
Dongcheng Zhao, Yi Zeng, Yang Li
Spiking neural networks (SNNs) transmit information through discrete spikes, which performs well in processing spatial-temporal information. Due to the non-differentiable character…
BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neurons
Yang Li, Yi Zeng, Dongcheng Zhao
The spiking neural network (SNN) computes and communicates information through discrete binary events. It is considered more biologically plausible and more energy-efficient than a…
Quantum Superposition Inspired Spiking Neural Network
Yinqian Sun, Yi Zeng, Tielin Zhang
Despite advances in artificial intelligence models, neural networks still cannot achieve human performance, partly due to differences in how information is encoded and processed co…
Responsible Facial Recognition and Beyond
Yi Zeng, Enmeng Lu, Yinqian Sun +1
Facial recognition is changing the way we live in and interact with our society. Here we discuss the two sides of facial recognition, summarizing potential risks and current concer…
FlexNER: A Flexible LSTM-CNN Stack Framework for Named Entity Recognition
Hongyin Zhu, Wenpeng Hu, Yi Zeng
Named entity recognition (NER) is a foundational technology for information extraction. This paper presents a flexible NER framework compatible with different languages and domains…
Linking Artificial Intelligence Principles
Yi Zeng, Enmeng Lu, Cunqing Huangfu
Artificial Intelligence principles define social and ethical considerations to develop future AI. They come from research institutes, government organizations and industries. All v…