The dynamic impact of adult neurogenesis on pattern separation within the dentate gyrus neural network

Pattern separation in the dentate gyrus (DG) is crucial for distinguishing similar memories. The DG continues to undergo neurogenesis throughout the lifespan, and adult hippocampus neurogenesis leads to the incorporation of thousands of adult-born granule cells (adult-born GCs) into the existing DG circuitry. These newborn GCs exhibit high excitability and are easier to respond to novel stimuli, which seems to be contrary to the requirement of pattern separation for high input specificity. Meanwhile, the changes brought about by the growth of adult-born GCs can not be ignored. Here, we build a biologically relevant model of the DG containing adult-born GCs and test it using the Modified National Institute of Standards and Technology (MNIST) database. By analyzing this model, the results show that the net effect of adult-born GCs to GCs is inhibition, thereby improving the sparsity of GCs and pattern separation. This provides computational evidence for "indirect encoding" of adult-born GCs. In addition, as adult-born GCs transition toward maturity, they have the following growth characteristics: decreased activity, increased coupling strength with feedback inhibition, and enhanced synaptic plasticity. We find that the decreased activity reduces pattern separation efficiency while the other characteristics increase pattern separation efficiency. Finally, given that the firing rate of entorhinal cortex (EC) neurons is influenced by numerous factors (such as the complexity of memory tasks), the input frequency to the DG should be within a range rather than being fixed. To address this, we gradually increase the input frequency and notice that the presence of adult-born GCs increases the adaptability of the DG neural network and thus improves the robustness of pattern separation.