1 citations · 3 across the 11 of their papers we have counts for
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STF: Shallow-Level Temporal Feedback to Enhance Spiking Transformers
Zeqi Zheng, Zizheng Zhu, Yingchao Yu +5
Transformer-based Spiking Neural Networks (SNNs) suffer from a great performance gap compared to floating-point \mbox{Artificial} Neural Networks (ANNs) due to the binary nature of…
TDFormer: A Top-Down Attention-Controlled Spiking Transformer
Zizheng Zhu, Yingchao Yu, Zeqi Zheng +2
Traditional spiking neural networks (SNNs) can be viewed as a combination of multiple subnetworks with each running for one time step, where the parameters are shared, and the memb…
SpiLiFormer: Enhancing Spiking Transformers with Lateral Inhibition
Zeqi Zheng, Yanchen Huang, Yingchao Yu +4
Spiking Neural Networks (SNNs) based on Transformers have garnered significant attention due to their superior performance and high energy efficiency. However, the spiking attentio…
IP-RSNN: Bi-level Intrinsic Plasticity Enables Learning-to-learn in Recurrent Spiking Neural Networks
Yingchao Yu, Yaochu Jin, Kuangrong Hao +5
Learning-to-learn (L2L), defined as progressively faster learning across similar tasks, is fundamental to both neuroscience and artificial intelligence. However, its neural basis r…