Although current research aims to use and improve deep learning networks by applying knowledge about the structure and function of the healthy human brain and vice versa, the potential of using such networks to model neurodegenerative diseases remains largely understudied. In this work, we present a novel feasibility study modeling dementia in silico with deep convolutional neural networks. Therefore, deep convolutional neural networks were fully trained to perform visual object recognition, and then progressively injured in two distinct ways. More precisely, damage was progressively inflicted mimicking neuronal as well as synaptic injury. Synaptic injury was applied by randomly deleting weights in the network, while neuronal injury was simulated by removing full nodes or filters in the network. After each iteration of injury, network object recognition accuracy was evaluated. Saliency maps were generated using the uninjured and injured networks and quantitatively compared using the structural similarity index measure for test set images to further investigate the loss of visual cognition. The quantitative evaluation revealed cognitive function of the network progressively decreased with increasing injury load. This effect was more pronounced for synaptic damage. As damage increased, the model focus shifted away from the main objects in the images and became more dispersed. This shift in attention was quantitatively evidenced by a decrease in the structural similarity index measure comparing the saliency maps of corresponding uninjured and injured models, as a function of injury. The results of this study provide a promising foundation to develop in silico models of neurodegenerative diseases using deep learning networks. The effects of neurodegeneration found for the in silico model are especially similar to the loss of visual cognition seen in patients with posterior cortical atrophy.