Encoding scheme-dependent effects of input pattern pairs on pattern separation in the dentate gyrus neuronal network

The ability to discriminate highly similar memories depends critically on pattern separation mediated by the dentate gyrus (DG). Although intrinsic neural mechanisms have been extensively studied, the influence of external input patterns on this process is often overlooked. Here, we established a biologically plausible DG model, subjected it to input pattern pairs with varying degrees of overlap, and examined the firing properties of granule cells (GCs) as output patterns. Based on the analysis of population coding, we observed that as the overlap between input pairs decreased, both the separation distance and orthogonalization level increased, indicating enhanced separation performance. Subsequently, from the perspective of rate coding, we computed the mean firing rate of individual GC to paired inputs and statistically analyzed the distributions of mean firing rates and their pairwise differences across GCs. Our results showed that these firing rate metrics unaffected by variations in input overlap. However, the Pearson correlation coefficient and mutual information between GC ensembles were significantly influenced, with both measures decreasing as input overlap is reduced. We also found that high-overlap inputs ( $>70\mathrm{\%}$) induced substantial firing rate variability in individual GC, which predominantly manifested as rate remapping. Furthermore, clustering analysis based on time-windowed firing rates demonstrated that pattern separation does not alter the structural characteristics of GCs, with similar cluster configurations emerging across different input overlap conditions. Together, these findings provided a detailed characterization of GC firing properties during pattern separation.