From the 1 of 39 linked papers with an AI index.
8 papers · 1 filter
TEFormer: Structured Bidirectional Temporal Enhancement Modeling in Spiking Transformers
Sicheng Shen, Mingyang Lv, Bing Han +4
In recent years, Spiking Neural Networks (SNNs) have achieved remarkable progress, with Spiking Transformers emerging as a promising architecture for energy-efficient sequence mode…
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…
: 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…
Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks
Bing Han, Feifei Zhao, Yi Zeng +1
Developmental plasticity plays a prominent role in shaping the brain's structure during ongoing learning in response to dynamically changing environments. However, the existing net…
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…
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…