Effect of Adult-Born Immature Granule Cells on Pattern Separation in The Hippocampal Dentate Gyrus

TL;DR

This study uses a spiking neural network model to explore how adult-born immature granule cells influence pattern separation in the hippocampal dentate gyrus, revealing complex effects of their excitability and innervation on neural coding.

Contribution

It introduces a detailed neural network model incorporating immature granule cells to analyze their dual roles in pattern separation and integration in the hippocampus.

Findings

Immature GCs increase activity due to high excitability, leading to pattern integration.

Mature GCs exhibit sparse firing, resulting in high pattern separation efficacy.

Heterogeneity from immature GCs can reduce overall pattern separation performance.

Abstract

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.