Criticality in in silico and in vitro neuronal networks

Abstract

Neuronal networks are hypothesized to operate near a critical state—an intermediate regime between order and disorder—where information processing is optimized. This thesis investigates criticality in neuronal systems using a threefold approach: (i) a branching process model to reproduce avalanche dynamics with power-law statistics; (ii) simulations of spiking activity in spatially embedded networks using Random Geometric Graphs (RGGs) together with the Izhikevich dynamic neuronal model, to explore how modular topology promotes critical behavior; and (iii) analysis of electrophysiological recordings from human induced pluripotent stem cell (hiPSC) derived neuronal cultures. Our findings reveal that both simulated and experimental data exhibit scale-invariant avalanche statistics and satisfy universal exponent relations characteristic of critical systems. Observed deviations from mean-field theoretical predictions are attributed to spatial constraints and connectivity density. These results support the hypothesis that criticality emerges robustly in structurally diverse neuronal architectures while preserving core dynamical features