A residual neural-network model to predict visual cortex measurements

Understanding how the visual cortex of the human brain really works is still an open problem for science today. A better understanding of natural intelligence could also benefit object-recognition algorithms based on convolutional neural networks. In this paper we demonstrate the asset of using a residual neural network of only 20 layers for this task. The advantage of this limited number is that earlier stages of the network can be more easily trained, which allows us to add more layers at the earlier stage. With this additional layer the prediction of the visual brain activity improves from 10.4% to 15.53%.