  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 multi-region 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.
