35 citations · 35 across the 2 of their papers we have counts for
6 papers
Spikingformer: A Key Foundation Model for Spiking Neural Networks
Chenlin Zhou, Liutao Yu, Zhaokun Zhou +5
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks, due to their event-driven spiking computation. However, some foundation…
Multiplication-Free Parallelizable Spiking Neurons with Efficient Spatio-Temporal Dynamics
Peng Xue, Wei Fang, Zhengyu Ma +5
Spiking Neural Networks (SNNs) are distinguished from Artificial Neural Networks (ANNs) for their complex neuronal dynamics and sparse binary activations (spikes) inspired by the b…
HDI-Former: Hybrid Dynamic Interaction ANN-SNN Transformer for Object Detection Using Frames and Events
Dianze Li, Jianing Li, Xu Liu +3
Combining the complementary benefits of frames and events has been widely used for object detection in challenging scenarios. However, most object detection methods use two indepen…
Spatial-Temporal Search for Spiking Neural Networks
Kaiwei Che, Zhaokun Zhou, Li Yuan +3
Spiking Neural Networks (SNNs) are considered as a potential candidate for the next generation of artificial intelligence with appealing characteristics such as sparse computation…
QKFormer: Hierarchical Spiking Transformer using Q-K Attention
Chenlin Zhou, Han Zhang, Zhaokun Zhou +7
Spiking Transformers, which integrate Spiking Neural Networks (SNNs) with Transformer architectures, have attracted significant attention due to their potential for energy efficien…
Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods
Chenlin Zhou, Han Zhang, Liutao Yu +7
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-te…