The advent of large-scale and high-density ex-vivo experiments enables the unveiling of neural network dynamics at unprecedented resolution. It is now routinely possible to grow neural network cultures from individual human subjects, allowing us to compare the dynamics of subtly different genetic backgrounds. As the analysis of network dynamics becomes more fine-grained, we observed unexplained variability of our results during the development stages of the cultures, beyond the boundaries of genotypic differences. Our work leveraged breakthroughs in culturing cortical neurons from human induced pluripotent stem cells (hiPSCs) recorded with high-density microelectrode arrays (HD-MEAs) containing 26,500 electrodes. After spike-sorting we compared the neural dynamics of epilepsy patient-derived networks with those derived from genetically related control subjects at multiple developmental stages of the cultured neurons. Utilizing functionality connectivity analysis, burst profile algorithms, and a comprehensive library of metrics, we noticed considerable variability within and between groups, across different cultures and time points, between family members, and within cultures from the same individuals. This means that unexplained variability exists despite shared or even identical genetic backgrounds, indicating that genetics alone cannot fully account for the observed differences in neural activity. To consistently identify genotypical activity, we engineered a multifaceted analysis combining various metrics at different developmental stages, allowing us to draw an intricate picture of neural dynamics in cell cultures. We discuss how this combination of metrics can help us to identify electrophysiological markers of disorders such as epilepsy, and pave the way for more targeted and effective treatments.