Methods to find patterns or correlations in MEA spike trains I am looking for methods currently used to analyze micro-electrode arrays spike trains and figure out patterns, as repeated sequences of activation or spacial correlations.

I know about ISI histograms, power spectrum, anda DNA-sequencing-like-algorithms, but I am looking for other methods, from statistics or machine learning. 
Any recommendations of papers/books exploiting these methods?
This is a very broad question. There is a very large literature on studying MEA spike trains: some classic methods are probably already explained in books such as Dayan and Abbott's Theoretical Neuroscience. 

If you're looking for "statistical modelling", i.e. describing properties of a network's activity without caring for its actual structure and dynamics, generalised linear models (GLM) are a common tool (see articles by Liam Paninski 2004, Jonathan Pillow 2008) and maximum entropy models too (Schneidman et al. 2006, Shlens et al 2006). This literature is still quite popular, although probably not as fruitful as a decade ago.

There are certainly other things that could be mentioned in this area, but I don't know them all, and it also fundamentally depends on what you're trying to do. Hope this helps.