Recurrence is required to capture the representational dynamics of the human visual system
Recurrence is required to capture the representational dynamics of the human visual system
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DOI：
10.1073/pnas.1905544116
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发表时间：
2019-10-22
影响因子：
11.1
通讯作者：
Kriegeskorte, Nikolaus
中科院分区：
文献类型：
--
作者：
Kietzmann, Tim C.;Spoerer, Courtney J.;Kriegeskorte, Nikolaus
人类视觉系统是一个复杂的大脑区域网络，使我们能够识别周围的世界。尽管它丰富的横向和反馈连接，对象处理通常被视为一个前馈过程和研究。在这里，我们使用时间分辨的大脑成像和深度学习来测量和建模人类腹侧流多个阶段的快速表征动态。我们观察到实质性的代表性的转变，在第一个300毫秒内和跨腹流区域的处理。分类划分是按顺序出现的，在各地区之间是向前和向后级联的，格兰杰因果关系分析表明，各地区之间存在双向信息流动。最后，递归深度神经网络模型在捕获多区域皮层动态的能力方面明显优于参数匹配的前馈模型。在循环深层网络模型上进行的有针对性的虚拟冷却实验进一步证实了它们的横向和自上而下连接的重要性。这些结果表明，需要经常性的模型来理解人类腹侧流的信息处理。
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.