Conclusion
Existing data analysis tools like cross-correlograms, JPSTH and PCA do not scale well as we look at several neurons at a time. Our approach provides an efficient and formal basis for learning probabilistic models from observed spike train data. Several types of network dynamics like syn-fire chains, polychrony [5] etc. that neuronal networks are known to exhibit can be modeled as excitatory networks and hence their putative structure can be learnt using our method (as illustrated in Figure 2). Our proposed approach also scales very well to large data sizes as it marries fast data mining style algorithms with formal model learning.