RT Journal Article
SR Electronic
T1 Directly interfacing brain and deep networks exposes non-hierarchical visual processing
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.06.28.450213
DO 10.1101/2021.06.28.450213
A1 Sexton, Nicholas J.
A1 Love, Bradley C.
YR 2021
UL http://biorxiv.org/content/early/2021/06/29/2021.06.28.450213.abstract
AB One reason the mammalian visual system is viewed as hierarchical, such that successive stages of processing contain ever higher-level information, is because of functional correspondences with deep convolutional neural networks (DCNNs). However, these correspondences between brain and model activity involve shared, not task-relevant, variance. We propose a stricter test of correspondence: If a DCNN layer corresponds to a brain region, then replacing model activity with brain activity should successfully drive the DCNN’s object recognition decision. Using this approach on three datasets, we found all regions along the ventral visual stream best corresponded with later model layers, indicating all stages of processing contained higher-level information about object category. Time course analyses suggest long-range recurrent connections transmit object class information from late to early visual areas.Competing Interest StatementThe authors have declared no competing interest.