Adult hippocampal neurogenesis (AHN) has been considered to effectively participate in the dentate gyrus (DG) network to strengthen the function of pattern separation. Although the potential role of neurogenesis in pattern separation has been theoretically studied,the detailed effects of newborn granule cells on information processing and network regulation are still under debate. For the aforementioned difficulty,this work introduce 4-6-week newborn granule cells as independent information processing units and propose a novel computational model of the DG network with neurogenesis. This work investigate the contribution of newborn granule cells to pattern separation under different input stimuli. With the aid of simulation results,it show that newborn granule cells play different roles in the DG network when receiving different intensities of stimulation. Under low intensity stimulus,newborn granule cells can restore the information representation ability of the network and avoid pattern separation failure by taking advantage of their easily activated neuronal properties. Under high intensity stimulus,as a kind of interneurons,newborn granule cells can enhance the feedback inhibition effect of local circuits to improve the sparsity of mature granule cells,and ultimately improve the function of pattern separation. Therefore,this model predicts a critical role of adult hippocampal neurogenesis in pattern separation robustness under more subtle and extensive input.