A theoretical network model to analyse neurogenesis and synaptogenesis in the dentate gyrus.

We describe a strongly biologically motivated artificial neural network approach to model neurogenesis and synaptic turnover as it naturally occurs for example in the hippocampal dentate gyrus (DG) of the developing and adult mammalian and human brain. The results suggest that cell proliferation (CP) has not only a functional meaning for computational tasks and learning but is also relevant for maintaining homeostatic stability of the neural activity. Moderate rates of CP buffer disturbances in input activity more effectively than networks without or very high CP. Up to a critical mark an increase of CP enhances synaptogenesis which might be beneficial for learning. However, higher rates of CP are rather ineffective as they destabilize the network: high CP rates and a disturbing input activity effect a reduced cell survival. By these results the simulation model sheds light on the recurrent interdependence of structure and function in biological neural networks especially in hippocampal circuits and the interacting morphogenetic effects of neurogenesis and synaptogenesis.