We present a novel and scalable approach to accurately identify neuronal ensembles in spiking neuron populations. This method, which is part of a previously published methodology, uses minimal parameter tuning requirements and improved computational efficiency, making it a valuable tool for researchers studying complex ensemble activity in neural circuits. Using clustering of synchronous activities, our methodology allows neurons to be part of multiple ensembles, demonstrating its effectiveness across a wide range of simulation parameters. We also demonstrate the versatility of this technique, applying it to both artificially generated data and spike trains obtained from retinal ganglion cells via multielectrode array recordings. A comparison of the results reveals the superior performance of the method and its wider applicability compared to other prevalent techniques in the field. Our investigations uncover a consistent pattern of stimuli-induced activity, in addition to spontaneously active and sporadic ensembles. These findings suggest the potential compartmentalization of the early visual system into specific functional ensembles. To make this innovative method more accessible and easier to use, we provide a user-friendly graphical interface in this chapter. Our goal is to provide readers with a comprehensive understanding and the resources needed to implement a reliable technique, thus advancing our collective knowledge of the intricate functional networks of the central nervous system.