The aim of this work was to enhance the biological feasibility of a deep convolutional neural network-based in-silico model of neurodegeneration of the visual system by adding neuroplasticity to it. Therefore, deep convolutional networks were trained for object recognition tasks and progressively lesioned to simulate the onset of posterior cortical atrophy, a condition that affects the visual cortex in patients with Alzheimer’s disease (AD). After each iteration of injury, the networks were retrained on the training set to simulate the continual plasticity of the human brain, when affected by a neurodegenerative disease. More specifically, the injured parts of the network remained injured while we investigated how the added retraining steps were able to recover some of the model’s baseline performance. The results showed that with retraining, a model’s object recognition abilities are subject to a smoother decline with increasing injury levels than without retraining and, therefore, more similar to the longitudinal cognition impairments of patients diagnosed with AD. Moreover, with retraining, the injured model exhibits internal activation patterns similar to those of the healthy baseline model compared to the injured model without retraining. In conclusion, adding retraining to the in-silico setup improves the biological feasibility considerably and could prove valuable to test different rehabilitation approaches in-silico.