期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 116, 期 43, 页码 21854-21863出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1905544116
关键词
object recognition; deep recurrent neural networks; representational dynamics; magnetoencephalography; virtual cooling
资金
- UK Medical Research Council [MC-A060-5PR20]
- European Research Council [ERC-2010-StG 261352]
- Human Brain Project (European Union) [604102]
- Deutsche Forschungsgemeinschaft
- MRC [MC_U105597120, MC_UU_00005/14, MC_U105579212] Funding Source: UKRI
- Medical Research Council [MC_UU_00005/14, MC_U105579212] Funding Source: researchfish
The human visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, object processing is commonly viewed and studied as a feedforward process. Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning. We observe substantial representational transformations during the first 300 ms of processing within and across ventral-stream regions. Categorical divisions emerge in sequence, cascading forward and in reverse across regions, and Granger causality analysis suggests bidirectional information flow between regions. Finally, recurrent deep neural network models clearly outperform parameter-matched feedforward models in terms of their ability to capture the multiregion cortical dynamics. Targeted virtual cooling experiments on the recurrent deep network models further substantiate the importance of their lateral and top-down connections. These results establish that recurrent models are required to understand information processing in the human ventral stream.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据