RT Journal Article
SR Electronic
T1 Activations of Deep Convolutional Neural Network are Aligned with Gamma Band Activity of Human Visual Cortex
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 133694
DO 10.1101/133694
A1 Ilya Kuzovkin
A1 Raul Vicente
A1 Mathilde Petton
A1 Jean-Philippe Lachaux
A1 Monica Baciu
A1 Philippe Kahane
A1 Sylvain Rheims
A1 Juan R. Vidal
A1 Jaan Aru
YR 2018
UL http://biorxiv.org/content/early/2018/05/02/133694.abstract
AB Previous work demonstrated a direct correspondence between the hierarchy of the human visual areas and layers of deep convolutional neural networks (DCNN) trained on visual object recognition. We used DCNNs to investigate which frequency bands correlate with feature transformations of increasing complexity along the ventral visual pathway. By capitalizing on intracranial depth recordings from 100 patients and 11293 electrodes we assessed the alignment between the DCNN and signals at different frequency bands in different time windows. We found that gamma activity, especially in the low gamma-band (30 – 70 Hz), matched the increasing complexity of visual feature representations in the DCNN. These findings show that the activity of the DCNN captures the essential characteristics of biological object recognition not only in space and time, but also in the frequency domain. These results also demonstrate the potential that modern artificial intelligence algorithms have in advancing our understanding of the brain.Significance Statement Recent advances in the field of artificial intelligence have revealed principles about neural processing, in particular about vision. Previous works have demonstrated a direct correspondence between the hierarchy of human visual areas and layers of deep convolutional neural networks (DCNNs), suggesting that DCNN is a good model of visual object recognition in primate brain. Studying intracranial depth recordings allowed us to extend previous works by assessing when and at which frequency bands the activity of the visual system corresponds to the DCNN. Our key finding is that signals in gamma frequencies along the ventral visual pathway are aligned with the layers of DCNN. Gamma frequencies play a major role in transforming visual input to coherent object representations.