4 citations · 14 across the 27 of their papers we have counts for
9 papers · 1 filter
STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking
Sicheng Shen, Dongcheng Zhao, Linghao Feng +5
Spiking Transformers have recently emerged as promising architectures for combining the efficiency of spiking neural networks with the representational power of self-attention. How…
Biologically Inspired Spiking Diffusion Model with Adaptive Lateral Selection Mechanism
Linghao Feng, Dongcheng Zhao, Sicheng Shen +1
Lateral connection is a fundamental feature of biological neural circuits, facilitating local information processing and adaptive learning. In this work, we integrate lateral conne…
: Enhanced Information Flow in Spiking Neural Networks with High Hardware Compatibility
Guobin Shen, Jindong Li, Tenglong Li +2
Spiking Neural Networks (SNNs) hold promise for energy-efficient, biologically inspired computing. We identify substantial informatio loss during spike transmission, linked to temp…
Time Cell Inspired Temporal Codebook in Spiking Neural Networks for Enhanced Image Generation
Linghao Feng, Dongcheng Zhao, Sicheng Shen +3
This paper presents a novel approach leveraging Spiking Neural Networks (SNNs) to construct a Variational Quantized Autoencoder (VQ-VAE) with a temporal codebook inspired by hippoc…
Neuro-Vision to Language: Enhancing Brain Recording-based Visual Reconstruction and Language Interaction
Guobin Shen, Dongcheng Zhao, Xiang He +5
Decoding non-invasive brain recordings is pivotal for advancing our understanding of human cognition but faces challenges due to individual differences and complex neural signal re…
Parallel Spiking Unit for Efficient Training of Spiking Neural Networks
Yang Li, Yinqian Sun, Xiang He +3
Efficient parallel computing has become a pivotal element in advancing artificial intelligence. Yet, the deployment of Spiking Neural Networks (SNNs) in this domain is hampered by…
TIM: An Efficient Temporal Interaction Module for Spiking Transformer
Sicheng Shen, Dongcheng Zhao, Guobin Shen +1
Spiking Neural Networks (SNNs), as the third generation of neural networks, have gained prominence for their biological plausibility and computational efficiency, especially in pro…
Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling
Guobin Shen, Dongcheng Zhao, Yiting Dong +4
Within the complex neuroarchitecture of the brain, astrocytes play crucial roles in development, structure, and metabolism. These cells regulate neural activity through tripartite…
Is Conventional SNN Really Efficient? A Perspective from Network Quantization
Guobin Shen, Dongcheng Zhao, Tenglong Li +2
Spiking Neural Networks (SNNs) have been widely praised for their high energy efficiency and immense potential. However, comprehensive research that critically contrasts and correl…