A Spatial–Channel–Temporal-Fused Attention for Spiking Neural Networks-Reference-Cited by-同舟云学术

A Spatial–Channel–Temporal-Fused Attention for Spiking Neural Networks

Published:2023 Issue: Volume: Page:1-15
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Cai Wuque¹,Sun Hongze¹,Liu Rui¹^ORCID,Cui Yan²^ORCID,Wang Jun¹,Xia Yang¹,Yao Dezhong¹^ORCID,Guo Daqing¹^ORCID

Affiliation:

1. Ministry of Education (MOE) Key Laboratory for NeuroInformation, School of Life Science and Technology, Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China

2. Department of Neurosurgery, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China

Funder

STI 2030–Major Project

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10138927.pdf?arnumber=10138927

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1. Reliable object tracking by multimodal hybrid feature extraction and transformer-based fusion;Neural Networks;2024-10

2. An Event-based Feature Representation Method for Event Stream Classification using Deep Spiking Neural Networks;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

3. STCA-SNN: self-attention-based temporal-channel joint attention for spiking neural networks;Frontiers in Neuroscience;2023-11-10