Computational Modeling of Adult Neurogenesis

Abstract

The restriction of adult neurogenesis to only a handful of regions of the brain is suggestive of some shared requirement for this dramatic form of structural plasticity. However, a common driver across neurogenic regions has not yet been identified. Computational studies have been invaluable in providing insight into the functional role of new neurons; however, researchers have typically focused on specific scales ranging from abstract neural networks to specific neural systems, most commonly the dentate gyrus area of the hippocampus. These studies have yielded a number of diverse potential functions for new neurons, ranging from an impact on pattern separation to the incorporation of time into episodic memories to enabling the forgetting of old information. This review will summarize these past computational efforts and discuss whether these proposed theoretical functions can be unified into a common rationale for why neurogenesis is required in these unique neural circuits.

Figure 1.

Overview of different scales relevant to characterizing neurogenesis function in the dentate gyrus (DG). (A) Understanding neurogenesis function involves considering how dynamics of a cellular process propagate up to the behavioral level. (B) There are currently several theories for each of these scale transitions, providing both a framework for understanding DG neurogenesis, as well as a challenge in updating classic models to include new neurons.

Figure 2.

Overview of potential mechanisms for neurogenesis effect on pattern separation. (A) Common hypothesis in which increased activity and plasticity of new neurons represent novel information, allowing progressive events to be better separated (left). Without neurogenesis, a mature population must represent all events (right). (B) Alternative mechanism whereby young neurons increase feedback inhibition in the dentate gyrus (DG) (left). Without new neurons, mature neuron activity is higher, decreasing separation (right). (C) Another mechanism in which young neurons activate feedforward inhibition in the CA3, which decreases the density of attractors and reduces interference. Notably, none of these mechanisms are exclusive and all may be occurring simultaneously.