Name
Title | ||
|---|---|---|
Towards Chip-on-Chip Neuroscience: Fast Mining of Frequent Episodes Using Graphics Processors |
Abstract | ||
|---|---|---|
Computational neuroscience is being revolutionized with the advent of multi-electrode arrays that provide real-time, dynamic, perspectives into brain function. Mining event streams from these chips is critical to understanding the firing patterns of neurons and to gaining insight into the underlying cellular activity. We present a GPGPU solution to mining spike trains. We focus on mining frequent episodes which captures coordinated events across time even in the presence of intervening background/"junk" events. Our algorithmic contributions are two-fold: MapConcatenate, a new computation- to-core mapping scheme, and a two-pass elimination approach to quickly find supported episodes from a large number of candidates. Together, they help realize a real-time "chip-on-chip" solution to neuroscience data mining, where one chip (the multi-electrode array) supplies the spike train data and another (the GPGPU) mines it at a scale unachievable previously. Evaluation on both synthetic and real datasets demonstrate the potential of our approach. |
Year | Venue | Keywords |
|---|---|---|
2009 | Clinical Orthopaedics and Related Research | frequent episode mining,computational neuroscience.,spike train datasets,gpgpu,chip,data mining,computational neuroscience,real time |
Field | DocType | Volume |
Graphics,Computational neuroscience,Neuroscience,Spike train,Computer science,CUDA,Chip,General-purpose computing on graphics processing units,Train,Distributed computing | Journal | abs/0905.2 |
Citations | PageRank | References |
2 | 0.45 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Cao | 1 | 68 | 10.33 |
| Debprakash Patnaik | 2 | 191 | 14.89 |
| Sean P. Ponce | 3 | 73 | 5.54 |
| Jeremy S. Archuleta | 4 | 63 | 5.05 |
| Patrick Butler | 5 | 173 | 11.71 |
| Wu-chun Feng | 6 | 2812 | 232.50 |
| Naren Ramakrishnan | 7 | 1913 | 176.25 |