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Discovering the 'Neural Code' from multi-neuronal spike trains is animportant task in neuroscience. For such an analysis, it is important tounearth interesting regularities in the spiking patterns. In this report, wepresent an efficient method for automatically discovering synchrony, synfirechains, and more general sequences of neuronal firings. We use the FrequentEpisode Discovery framework of Laxman, Sastry, and Unnikrishnan (2005), inwhich the episodes are represented and recognized using finite-state automata.Many aspects of functional connectivity between neuronal populations can beinferred from the episodes. We demonstrate these using simulated multi-neuronaldata from a Poisson model. We also present a method to assess the statisticalsignificance of the discovered episodes. Since the Temporal Data Mining (TDM)methods used in this report can analyze data from hundreds and potentiallythousands of neurons, we argue that this framework is appropriate fordiscovering the `Neural Code'.

Discovering Patterns in Multi-neuronal Spike Trains using the Frequent Episode Method