CASIA OpenIR  > 脑图谱与类脑智能实验室  > 类脑认知计算
Spiking Generative Adversarial Network with Attention Scoring Decoding
Feng, Linghao1,3; Zhao, Dongcheng1; Zeng, Yi1,2,3
Source PublicationNeural Networks
2024
Pages106423
Abstract

Generative models based on neural networks present a substantial challenge within deep learning. As it stands, such models are primarily limited to the domain of artificial neural networks. Spiking neural networks, as the third generation of neural networks, offer a closer approximation to brain-like processing due to their rich spatiotemporal dynamics. However, generative models based on spiking neural networks are not well studied. Particularly, previous works on generative adversarial networks based on spiking neural networks are conducted on simple datasets and do not perform well. In this work, we pioneer constructing a spiking generative adversarial network capable of handling complex images and having higher performance. Our first task is to identify the problems of out-of-domain inconsistency and temporal inconsistency inherent in spiking generative adversarial networks. We addressed these issues by incorporating the Earth-Mover distance and an attention-based weighted decoding method, significantly enhancing the performance of our algorithm across several datasets. Experimental results reveal that our approach outperforms existing methods on the MNIST, FashionMNIST, CIFAR10, and CelebA. In addition to our examination of static datasets, this study marks our inaugural investigation into event-based data, through which we achieved noteworthy results. Moreover, compared with hybrid spiking generative adversarial networks, where the discriminator is an artificial analog neural network, our methodology demonstrates closer alignment with the information processing patterns observed in the mouse. Our code can be found at https://github.com/Brain-Cog-Lab/sgad.

MOST Discipline Catalogue工学::控制科学与工程
DOIhttps://doi.org/10.1016/j.neunet.2024.106423
URL查看原文
Indexed BySCI
Language英语
Sub direction classification类脑模型与计算
planning direction of the national heavy laboratory认知机理与类脑学习
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57259
Collection脑图谱与类脑智能实验室_类脑认知计算
Affiliation1.Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences
2.Center for Excellence in Brain Science and Intelligence Technology, CAS,
3.School of Future Technology, University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Feng, Linghao,Zhao, Dongcheng,Zeng, Yi. Spiking Generative Adversarial Network with Attention Scoring Decoding[J]. Neural Networks,2024:106423.
APA Feng, Linghao,Zhao, Dongcheng,&Zeng, Yi.(2024).Spiking Generative Adversarial Network with Attention Scoring Decoding.Neural Networks,106423.
MLA Feng, Linghao,et al."Spiking Generative Adversarial Network with Attention Scoring Decoding".Neural Networks (2024):106423.
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