AbstractPattern separation is a mnemonic process that has been extensively studied over the years. It entails the ability ‐of primarily hippocampal circuits‐ to distinguish between highly similar inputs, via generating different neuronal activity (output) patterns. The dentate gyrus (DG) in particular has long been hypothesized to implement pattern separation by detecting and storing similar inputs as distinct representations. The ways in which these distinct representations can be generated have been explored in a number of theoretical and computational modeling studies. Here, we review two categories of pattern separation models: those that address the phenomenon in an abstract mathematical fashion and those that delve into the underlying biological mechanisms by taking into account the anatomy and/or physiology of hippocampal circuits. We summarize the strategies, findings and limitations of these modeling approaches in the light of new experimental findings and propose a unifying framework whereby different network, cellular and sub‐cellular mechanisms converge to a common goal: controlling sparsity, the key determinant of pattern separation in the DG.