The hippocampus plays a key role in pattern separation, namely the process of transforming similar incoming information to highly dissimilar, non-overlapping representations. Sparse firing granule cells in the dentate gyrus have been proposed to undertake this computation, but little is known about which of their properties influence pattern separation. Dendritic atrophy and spine loss have been reported in diseases associated with pattern separation deficits, suggesting a possible role for dendrites in this phenomenon. To investigate whether and how the dendrites of granule cells contribute to pattern separation, we build a simplified, biologically relevant, computational model of the dentate gyrus. Our model suggests that the presence of granule cell dendrites is associated with high pattern separation efficiency while their atrophy leads to increased excitability and performance impairments that cannot be explained by input resistance changes. These impairments, however, can be rescued by a range of manipulations that restore network sparsity to control levels. Thus, our model suggests that the contribution of dendrites to pattern separation amounts to one of many ways for controlling sparsity. We provide a number of testable predictions that can help investigate this proposition experimentally.