In this study we explore network connectivity of in vitro neuronal systems of live biological cells within a real-time game environment of the game Pong. We use DishBrain - a system that embodies in vitro neural networks with in silico computation using a high-density multi-electrode array (MEA). We compare functional connectivity across resting-state sessions and gameplay sessions, during which the cells control a paddle able to hit a ball. The location of the paddle and ball are temporally and spatially encoded and presented to the neurons via electrical stimulation. We observe significant differences in connectivity between gameplay and rest; the former being correlated with greater, more integrated activity than the latter. Since neuronal spikes are highly sparse in their nature, we also perform dimensionality reduction on the output data, and compare functional connectivity results on the reduced dataset with that of the full dataset. We observe changes in overall connectivity of the lower-dimensional data both within-regions and across multiple regions on the MEA during gameplay, but minimal changes during rest. These changes reflect the trends exhibited by the higher-dimensional data, demonstrating that a lower-dimensional subset is capable of capturing key information from sparse spiking neural data. Our findings provide insights into the characteristics of dynamic learning of biological cells embodied in a structured information landscape. Our results indicate underlying principles of neural plasticity which may support further investigations into unique properties associated with biological learning.