Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2007 | Clinical Orthopaedics and Related Research | data mining,neuronal network |
Field | DocType | Volume |
Data mining,Spike train,Computer science,Artificial intelligence,Biological neural network,Machine learning | Journal | abs/0709.0 |
Citations | PageRank | References |
1 | 0.38 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Debprakash Patnaik | 1 | 191 | 14.89 |
| P. S. Sastry | 2 | 741 | 57.27 |
| K. P. Unnikrishnan | 3 | 299 | 23.21 |