Pattern separation in the hippocampus through the eyes of computational modeling

Pattern separation is a mnemonic process extensively studied over the years. It primarily entails hippocampal circuits' ability to distinguish between highly similar inputs via generating different neuronal activity (output) patterns. In particular, the dentate gyrus (DG) has long been hypothesized to implement pattern separation by detecting and storing similar inputs as distinct representations. Several theoretical and computational modeling studies have explored how these distinct representations can be generated. 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 considering 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 results 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.