87 citations · 236 across the 20 of their papers we have counts for
6 papers · 1 filter
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…