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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
Department of Computer Science, Virginia Tech, VA 24061, USA
arXiv:0905.2200 [cs.DC] (13 May 2009)

@article{2009arXiv0905.2200C,

   author={Cao}, Y. and {Patnaik}, D. and {Ponce}, S. and {Archuleta}, J. and {Butler}, P. and {Feng}, {W.-c.} and {Ramakrishnan}, N.},

   title={“{Towards Chip-on-Chip Neuroscience: Fast Mining of Frequent Episodes Using Graphics Processors}”},

   journal={ArXiv e-prints},

   archivePrefix={“arXiv”},

   eprint={0905.2200},

   primaryClass={“cs.DC”},

   keywords={Computer Science – Distributed, Parallel, and Cluster Computing, Computer Science – Databases},

   year={2009},

   month={may},

   adsurl={http://adsabs.harvard.edu/abs/2009arXiv0905.2200C},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

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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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