CASIA OpenIR  > 类脑智能研究中心  > 神经计算及脑机交互
Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models
Du Changde1,2; Li Jinpeng1,2; Huang Lijie1; He Huiguang1,2,3
Source PublicationEngineering
2019
Issue0Pages:1-8
Abstract

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.

KeywordBrain Encoding And Decoding Fmri Deep Neural Networks Deep Generative Models Dual Learning
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23623
Collection类脑智能研究中心_神经计算及脑机交互
Corresponding AuthorHe Huiguang
Affiliation1.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.
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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