Neuroscientists are collecting activation (spike-train) data from hundreds of neurons with milli-second precision. Analysis of these large data sets poses interesting data mining challenges. We describe computational methods and associated statistical significance tests to discover patterns in multi-neuronal spike trains. By discovering these patterns, we are able to uncover the functional connectivity (graphical structure) of the underlying neuronal networks and observe their time-evolutions. We illustrate these on simulated and real data sets and compare the data mining methods with model-based estimation methods. We conclude with a brief discussion of Hebb cell assemblies and neural codes and how data mining can help discover them.