Abstract
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 carry feature transformations of increasing complexity along the ventral visual pathway. By capitalizing on direct intracranial recordings from 81 patients and 9147 electrodes we assessed the alignment between the DCNN and signals at different frequency bands in different time windows. We found that activity in low and high gamma bands was aligned with the increasing complexity of visual feature representations in the DCNN. These findings show that activity in the gamma band is not only a correlate of object recognition, but carries increasingly complex features along the ventral visual pathway. Similar alignment was found in the alpha frequency highlighting an unexpected role for alpha in contributing to visual object recognition. These results 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 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 and alpha frequencies along the ventral visual pathway are aligned with the layers of DCNN. These frequencies play a major role in transforming visual input to coherent objects.