CNNs are a prime example of neuroscience influencing deep learning (LeCun, Bottou, Bengio, & Haffner, 1998). These neural networks are based on the seminal work done by Hubel and Wiesel (1962). They discovered that individual neuronal cells in the visual cortex responded only to the presence of visual features such as edges of certain orientations. From their experiments they deduced that the visual cortex contains a hierarchical arrangement of neuronal cells. These neurons are sensitive to specific subregions in the visual field, with these subregions being tiled to cover the entire visual field. They in fact act as localized filters over the input space, making them well suited to exploiting the strong spatial correlation found in natural images. CNNs have been immensely successful in many computer vision tasks not just because of the inspiration drawn from neuroscience, but also due to the clever engineering principles employed. Although they have traditionally been used for applications in the field of computer vision such as face recognition and image classification, CNNs have also been used in other areas such as speech recognition and natural language processing for certain tasks.