23 citations · 46 across the 7 of their papers we have counts for
7 papers
Streaming Algorithms for Pattern Discovery over Dynamically Changing Event Sequences
Debprakash Patnaik, Naren Ramakrishnan, Srivatsan Laxman +1
Discovering frequent episodes over event sequences is an important data mining task. In many applications, events constituting the data sequence arrive as a stream, at furious rate…
Accelerator-Oriented Algorithm Transformation for Temporal Data Mining
Debprakash Patnaik, Sean P. Ponce, Yong Cao +1
Temporal data mining algorithms are becoming increasingly important in many application domains including computational neuroscience, especially the analysis of spike train data. W…
Towards Chip-on-Chip Neuroscience: Fast Mining of Frequent Episodes Using Graphics Processors
Yong Cao, Debprakash Patnaik, Sean Ponce +4
Computational neuroscience is being revolutionized with the advent of multi-electrode arrays that provide real-time, dynamic, perspectives into brain function. Mining event streams…
Inferring Dynamic Bayesian Networks using Frequent Episode Mining
Debprakash Patnaik, Srivatsan Laxman, Naren Ramakrishnan
Motivation: Several different threads of research have been proposed for modeling and mining temporal data. On the one hand, approaches such as dynamic Bayesian networks (DBNs) pro…
Inferring Neuronal Network Connectivity from Spike Data: A Temporal Datamining Approach
Debprakash Patnaik, P. S. Sastry, K. P. Unnikrishnan
Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent developments in electrophysiology and imaging…
Discovering Patterns in Multi-neuronal Spike Trains using the Frequent Episode Method
K. P. Unnikrishnan, Debprakash Patnaik, P. S. Sastry
Discovering the 'Neural Code' from multi-neuronal spike trains is an important task in neuroscience. For such an analysis, it is important to unearth interesting regularities in th…