Knowledge Commons of Institute of Automation,CAS
Learning "What" and "Where": An Interpretable Neural Encoding Model | |
Wang, Haibao1,2; Huang, Lijie1; Du, Changde1,2; He, Huiguang1,2,3 | |
2019-08 | |
会议名称 | International Joint Conference on Neural Networks |
会议日期 | July 14-19, 2019 |
会议地点 | Budapest, Hungary |
摘要 | Neural encoding modeling aims to reveal how brain processes perceived information by establishing a quantitative relationship between stimuli and evoked brain activities. In the field of visual neuroscience, many studies have been dedicated to building the neural encoding model for primary visual cortex and demonstrate that the population receptive field (pRF) models can be used to explain how neurons in primary visual cortex work. However, these models rely on either the inflexible prior assumptions imposed on the spatial characteristics of pRF or the clumsy parameter estimation methods which requires too much manual adjustment. Suffering from these issues, current methods yield dissatisfactory performance on mimicking brain activity. In this paper, we address the problems under a novel “what” and “where” neural encoding framework. Basing on deep neural network (DNN) and the separability of the spatial (“where”) and visual feature (“what”) dimensions, the proposed method is not only powerful in extracting nonlinear features from images, but also rich in interpretability. Owing to two forms of regular- ization: sparsity and smoothness, receptive fields are estimated automatically for each voxel without prior assumptions on shape, which gets rid of the shortcomings of previous methods. Extensive empirical evaluations on publicly available fMRI dataset show that the proposed method has superior performance gains over several existing methods. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 人工智能基础理论 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39165 |
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 2.University of Chinese Academy of Sciences 3.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 | Wang, Haibao,Huang, Lijie,Du, Changde,et al. Learning "What" and "Where": An Interpretable Neural Encoding Model[C],2019. |
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