Knowledge Commons of Institute of Automation,CAS
Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models | |
Du Changde1,2; Li Jinpeng1,2; Huang Lijie1; He Huiguang1,2,3 | |
发表期刊 | Engineering |
2019 | |
期号 | 0页码:1-8 |
摘要 | Brain encoding and decoding via functional magnetic resonance imaging (fMRI) are two important aspects of visual perception neuroscience. Although previous researchers have made significant advances in brain encoding and decoding models, existing methods still require improvement using advanced machine learning techniques. For example, traditional methods usually build the encoding and decoding models separately, and are prone to overfitting on a small dataset. In fact, effectively unifying the encoding and decoding procedures may allow for more accurate predictions. In this paper, we first review the existing encoding and decoding methods and discuss the potential advantages of a “bidirectional” modeling strategy. Next, we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules. Furthermore, deep generative models (e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs)) have produced promising results in studies on brain encoding and decoding. Finally, we propose that the dual learning method, which was originally designed for machine translation tasks, could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data. |
关键词 | Brain Encoding And Decoding Fmri Deep Neural Networks Deep Generative Models Dual Learning |
语种 | 英语 |
WOS记录号 | WOS:000492056100024 |
七大方向——子方向分类 | 脑机接口 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23623 |
专题 | 脑图谱与类脑智能实验室_神经计算与脑机交互 |
通讯作者 | He Huiguang |
作者单位 | 1.Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China 2.University of Chinese Academy of Sciences, Beijing 100049, China 3.Center for Excellence in Brain Science and Intelligence Technology, CAS, Beijing, China. |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Du Changde,Li Jinpeng,Huang Lijie,et al. Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models[J]. Engineering,2019(0):1-8. |
APA | Du Changde,Li Jinpeng,Huang Lijie,&He Huiguang.(2019).Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models.Engineering(0),1-8. |
MLA | Du Changde,et al."Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models".Engineering .0(2019):1-8. |
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