  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 shallow residual neural network for this task. The benefit of this approach
is that earlier stages of the network can be accurately 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 $10.4\%$ (block 1) to
$15.53\%$ (last fully connected layer). By training the network for more than
10 epochs this improvement can become even larger.
