  Discovering frequent episodes in event sequences is an interesting data
mining task. In this paper, we argue that this framework is very effective for
analyzing multi-neuronal spike train data. Analyzing spike train data is an
important problem in neuroscience though there are no data mining approaches
reported for this. Motivated by this application, we introduce different
temporal constraints on the occurrences of episodes. We present algorithms for
discovering frequent episodes under temporal constraints. Through simulations,
we show that our method is very effective for analyzing spike train data for
unearthing underlying connectivity patterns.
