While deep neural networks have overwhelmingly established state-of-the-art results in many artificial intelligence problems, they can still be difficult to develop and debug. Recent research on deep learning understanding has focused on feature visualization, theoretical guarantees, model interpretability, and generalization. In this work, we analyze deep neural networks from a complementary perspective, focusing on convolutional models. We are interested in understanding the extent to which input signals may affect output features, and mapping features at any part of the network to the region in the input that produces them. The key parameter to associate an output feature to an input region is the receptive field of the convolutional network, which is defined as the size of the region in the input that produces the feature.