As one gains more intuitions and results on the importance of concerted activity in spike trains, models are developed to extract potential canonical principles underlying spike coding. These methods shed a new light on spike train dynamics. However, they require time and expertise to be implemented efficiently, making them hard to use in a daily basis by neuroscientists or modelers. To bridge this gap, we developed the license free multiplatform software ENAS (https://enas.inria.fr) integrating tools for individual and collective spike analysis and simulation, with some specificities devoted to the retina. It contains a toolbox allowing to handle Multi-Electrode Array (MEA) data: MEA grid geometry, spike analysis, and simultaneous visualisation of the stimulus. It allows also to compute receptive fields via Spike-Triggered Average (STA) and to represent them in 3 dimension (space and time). The core of ENAS is the statistical analysis of population codes. One of its main strength is to provide statistical analysis of spike trains using Maximum Entropy-Gibbs distributions taking into account both \textit{spatial and temporal correlations} as constraints, allowing to introduce causality and memory in statistics. Finally, ENAS also generates simulated spike trains. On one hand, one can draw a population raster from an user-specified Gibbs distribution. On the other hand, we have integrated in ENAS our retina simulator Virtual Retina, extended here to include lateral connections in the IPL. The methods used in this software have already been applied and validated to the analysis of retina data. All these tools are accessible through a friendly Graphical User Interface that avoids any scripting or writing code from user. Most of them have been implemented to run in parallel to reduce the time and memory consumption. We hope that ENAS will become a useful tool for neuroscientists to analyse spike trains and we hope to improve it thanks to user feedback. Our goal is to progressively enrich it with the latest research results, in order to facilitate transfer of new methods to the community.