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arXiv · 0905.2200

Towards Chip-on-Chip Neuroscience: Fast Mining of Frequent Episodes Using Graphics Processors

Yong Cao·Debprakash Patnaik·Sean Ponce·Jeremy Archuleta·Patrick Butler·Wu-chun Feng·Naren Ramakrishnan
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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.

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Yong Cao, Debprakash Patnaik, Sean Ponce, Jeremy Archuleta, Patrick Butler, Wu-chun Feng, Naren Ramakrishnan. 2009-05-13. Towards Chip-on-Chip Neuroscience: Fast Mining of Frequent Episodes Using Graphics Processors. https://arxiv.org/abs/0905.2200

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