A Three-Layered Agent-Based Model of Adult Hippocampal Neurogenesis (HANG-AB3L) with Stochastic Cell Fate Determination

Hippocampal adult neurogenesis (HANG) is a highly regulated process where neural stem cells progress through distinct stages—from Type 1 radial glia-like cells to mature neurons—via a complex series of proliferative and differentiative divisions. While recent in vivo imaging has provided valuable insights to cellular processes, the exact relationship between individual cell-fate decisions and long-term population stability remains difficult to quantify empirically. In this study, we utilized an agent-based (AB) model to simulate the stochastic dynamics of the hippocampal neurogenic niche. Our results demonstrate that while individual progenitor lineages exhibit high variability and probabilistic division symmetries (proliferative symmetric, asymmetric, and differentiative symmetric), the system achieves deterministic stability as the initial progenitor density increases. We found that the T ₁ progenitor pool follows a negative exponential decay profile, with its longevity primarily dictated by the differentiation rate ( α _{d , 0} ). Critically, the terminal output of immature neurons ( C _{IN , t} ) was non-linearly coupled to the proliferative capacity of transit-amplifying cells ( α _{pp , 0} ); even marginal increases in symmetric proliferative divisions resulted in an exponential expansion of the neuronal pool. These findings suggest that the homeostatic maintenance of the hippocampal niche is governed by a kinetic tuning of division probabilities, providing a theoretical bridge between single-cell stochasticity and robust tissue-level output.