  Young immature granule cells (imGCs) appear via adult neurogenesis in the
hippocampal dentate gyrus (DG). In comparison to mature GCs (mGCs) (born during
development), the imGCs exhibit two competing distinct properties such as high
excitability and low excitatory innervation. We develop a spiking neural
network for the DG, incorporating the imGCs, and investigate their effect on
pattern separation. We first consider the effect of high excitability. The
imGCs become very highly active due to their low firing threshold. Then, strong
pattern correlation occurs, which results in pattern integration. On the other
hand, the mGCs exhibit very sparse firing activity due to strongly increased
feedback inhibition. As a result of high sparsity, the pattern separation
efficacy (PSE) of the mGCs becomes very high. Thus, the whole population of GCs
becomes a heterogeneous one, composed of a (major) subpopulation of mGCs (i.e.,
pattern separators) with very low activation degree and a (minor) subpopulation
of imGCs (i.e., pattern integrators) with very high activation degree. In the
whole heterogeneous population, the overall activation degree of all the GCs is
a little reduced in comparison to the activation degree in the presence of only
mGCs without imGCs. However, no pattern separation occurs, due to heterogeneous
sparsity, in contrast to the usual intuitive thought that sparsity could
improve PSE. Next, we consider the effect of low excitatory innervation for the
imGCs. In this case, the imGCs perform pattern integration. The PSE of the mGCs
decreases from a high value to a limit value. In the whole population of all
the GCs, when passing through a threshold, pattern separation starts, the
overall PSE increases and approaches that of the mGCs. However, due to
heterogeneity caused by the imGCs, the overall PSE becomes deteriorated, in
comparison with that in the presence of only mGCs.
