Methods for identification of spike patterns in massively parallel spike trains

Temporally, precise correlations between simultaneously recorded neurons have been interpreted as signatures of cell assem-blies, i.e., groups of neurons that form processing units. Evidence for this hypothesis was found on the level of pairwisecorrelations in simultaneous recordings of few neurons. Increasing the number of simultaneously recorded neurons increasesthe chances to detect cell assembly activity due to the larger sample size. Recent technological advances have enabled therecording of 100 or more neurons in parallel. However, these massively parallel spike train data require novel statistical toolsto be analyzed for correlations, because they raise considerable combinatorial and multiple testing issues. Recently, variousof such methods have started to develop. First approaches were based on population or pairwise measures of synchronization,and later led to methods for the detection of various types of higher-order synchronization and of spatio-temporal patterns.The latest techniques combine data mining with analysis of statistical significance. Here, we give a comparative overview ofthese methods, of their assumptions and of the types of correlations they can detect