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The human visual system is an intricate network of brain regions that enablesus to recognize the world around us. Despite its abundant lateral and feedbackconnections, object processing is commonly viewed and studied as a feedforwardprocess. Here, we measure and model the rapid representational dynamics acrossmultiple stages of the human ventral stream using time-resolved brain imagingand deep learning. We observe substantial representational transformationsduring the first 300 ms of processing within and across ventral-stream regions.Categorical divisions emerge in sequence, cascading forward and in reverseacross regions, and Granger causality analysis suggests bidirectionalinformation flow between regions. Finally, recurrent deep neural network modelsclearly outperform parameter-matched feedforward models in terms of theirability to capture the multi-region cortical dynamics. Targeted virtual coolingexperiments on the recurrent deep network models further substantiate theimportance of their lateral and top-down connections. These results establishthat recurrent models are required to understand information processing in thehuman ventral stream.

Recurrence is required to capture the representational dynamics of the human visual system