  One of the methods used in image recognition is the Deep Convolutional Neural
Network (DCNN). DCNN is a model in which the expressive power of features is
greatly improved by deepening the hidden layer of CNN. The architecture of CNNs
is determined based on a model of the visual cortex of mammals. There is a
model called Residual Network (ResNet) that has a skip connection. ResNet is an
advanced model in terms of the learning method, but it has not been interpreted
from a biological viewpoint. In this research, we investigate the receptive
fields of a ResNet on the classification task in ImageNet. We find that ResNet
has orientation selective neurons and double opponent color neurons. In
addition, we suggest that some inactive neurons in the first layer of ResNet
affect the classification task.
