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Neural Encoding and Decoding with a Flow-based Invertible Generative Model | |
Qiongyi Zhou1,2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE Transactions on Cognitive and Developmental Systems (TCDS)
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2023 | |
卷号 | 15期号:2页码:724-736 |
摘要 | Recent studies on visual neural encoding and decoding have made significant progress,benefiting from the latest advances in deep neural networks having powerful representations. However,two challenges remain. First,the current decoding algorithms based on deep generative models always struggle with information losses,which may cause blurry reconstruction. Second,most studies model the neural encoding and decoding processes separately,neglecting the inherent dual relationship between the two tasks. In this paper,we propose a novel neural encoding and decoding method with a two-stage flow-based invertible generative model to tackle the above issues. First,a convolutional auto-encoder is trained to bridge the stimuli space and the feature space. Second,an adversarial cross-modal normalizing flow is trained to build up a bijective transformation between image features and neural signals,with local and global constraints imposed on the latent space to render cross-modal alignment. The method eventually achieves bi-directional generation of visual stimuli and neural responses with a combination of the flow-based generator and the auto-encoder. The flow-based invertible generative model can minimize information losses and unify neural encoding and decoding into a single framework. Experimental results on different neural signals containing spike signals and functional magnetic resonance imaging demonstrate that our model achieves the best comprehensive performance among the comparison models. |
DOI | 10.1109/TCDS.2022.3176977 |
WOS记录号 | WOS:001005746000036 |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 脑机接口 |
国重实验室规划方向分类 | 人工智能基础前沿理论 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50880 |
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 | Huiguang He |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.School of Computing, University of Leeds 4.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences |
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 | Qiongyi Zhou,Changde Du,Dan Li,et al. Neural Encoding and Decoding with a Flow-based Invertible Generative Model[J]. IEEE Transactions on Cognitive and Developmental Systems (TCDS),2023,15(2):724-736. |
APA | Qiongyi Zhou,Changde Du,Dan Li,Haibao Wang,Jian K. Liu,&Huiguang He.(2023).Neural Encoding and Decoding with a Flow-based Invertible Generative Model.IEEE Transactions on Cognitive and Developmental Systems (TCDS),15(2),724-736. |
MLA | Qiongyi Zhou,et al."Neural Encoding and Decoding with a Flow-based Invertible Generative Model".IEEE Transactions on Cognitive and Developmental Systems (TCDS) 15.2(2023):724-736. |
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