In this study, we characterize complex network dynamics in live in vitro neuronal systems during two distinct activity states: spontaneous rest state and engagement in a real-time (closed-loop) game environment using the DishBrain system.First, we embed the spiking activity of these channels in a lower-dimensional space using various representation learning methods and then extract a subset of representative channels. Next, by analyzing these low-dimensional representations, we explore the patterns of macroscopic neuronal network dynamics during learning. Remarkably, our findings indicate that just using the low-dimensional embedding of representative channels is sufficient to differentiate the neuronal culture during the Rest and Gameplay.Notably, our investigation shows dynamic changes in the connectivity patterns within the same region and across multiple regions on the multi-electrode array only during Gameplay. These findings underscore the plasticity of neuronal networks in response to external stimuli and highlight the potential for modulating connectivity in a controlled environment.The ability to distinguish between neuronal states using reduced-dimensional representations points to the presence of underlying patterns that could be pivotal for real-time monitoring and manipulation of neuronal cultures.Additionally, this provides insight into how biological based information processing systems rapidly adapt and learn and may lead to new improved algorithms.