SpikeAtConv: an integrated spiking-convolutional attention architecture for energy-efficient neuromorphic vision processing

SpikeAtConv: an integrated spiking-convolutional attention architecture for energy-efficient neuromorphic vision processing

IntroductionSpiking Neural Networks (SNNs) offer a biologically inspired alternative to conventional artificial neural networks, with potential advantages in power efficiency due to their event-driven computation. Despite their promise, SNNs have yet to achieve competitive performance on complex vis...

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Bibliographic Details
Main Authors: Wangdan Liao, Fei Chen, Changyue Liu, Weidong Wang, Hongyun Liu
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-03-01
Series:Frontiers in Neuroscience
Subjects:
spiking neural network
self-attention
convolutional neural network
deep learning
computer vision
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2025.1536771/full
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