RT Journal Article
SR Electronic
T1 Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.02.19.956748
DO 10.1101/2020.02.19.956748
A1 Yalda Mohsenzadeh
A1 Caitlin Mullin
A1 Benjamin Lahner
A1 Aude Oliva
YR 2020
UL http://biorxiv.org/content/early/2020/02/20/2020.02.19.956748.abstract
AB Research at the intersection of computer vision and neuroscience has revealed hierarchical correspondence between layers of deep convolutional neural networks (DCNNs) and cascade of regions along human ventral visual cortex. Recently, studies have uncovered emergence of human interpretable concepts within DCNNs layers trained to identify visual objects and scenes. Here, we asked whether an artificial neural network (with convolutional structure) trained for visual categorization would demonstrate spatial correspondences with human brain regions showing central/peripheral biases. Using representational similarity analysis, we compared activations of convolutional layers of a DCNN trained for object and scene categorization with neural representations in human brain visual regions. Results reveal a brain-like topographical organization in the layers of the DCNN, such that activations of layer-units with central-bias were associated with brain regions with foveal tendencies (e.g. fusiform gyrus), and activations of layer-units with selectivity for image backgrounds were associated with cortical regions showing peripheral preference (e.g. parahippocampal cortex). The emergence of a categorical topographical correspondence between DCNNs and brain regions suggests these models are a good approximation of the perceptual representation generated by biological neural networks.