Hierarchical Afferent Connectivity Drives Population-Wide Bursting Dynamics in a Computational Model of Human-Derived Excitatory Neuronal Networks.

This work presents a computational model of excitatory neuronal networks derived from human-induced pluripotent stem cells, whose activity was recorded with microelectrode arrays. A key feature of in vitro neuronal cultures is the emergence of network bursts (NBs)-population events involving most neurons, characterized by different durations, firing frequencies, and recruitment patterns. Our numerical approach investigates the mechanisms underlying these dynamics, addressing the limitations of experimental systems that make it difficult to isolate specific parameters and processes. The model aims to investigate how local neuronal dynamics and global structural connectivity interact to shape the emergence, propagation, and termination of NBs, highlighting the interdependence between intrinsic and network-level mechanisms. We demonstrate the critical role of noise in triggering NBs. At the same time, nonrandom, structured network topologies are essential for sustaining and shaping the resulting collective spatiotemporal firing patterns. In particular, we showed that the organization of incoming and outgoing degrees significantly modulates population recruitment and burst structure, with a hierarchical organization of afferent connectivity emerging as the dominant determinant of collective dynamics. By integrating in vitro observations into in silico simulations, the present study provides a solid foundation for understanding the principles governing human neuronal network function. Also, it sets the stage for investigating how alterations of network properties may contribute to pathological conditions.