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

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dc.creatorCao, Yong
dc.creatorPatnaik, Debprakash
dc.creatorPonce, Sean
dc.creatorArchuleta, Jeremy
dc.creatorButler, Patrick
dc.creatorFeng, Wu-chun
dc.creatorRamakrishnan, Naren
dc.date2009-05-13
dc.date.accessioned2026-07-07T13:14:54Z
dc.date.available2026-07-07T13:14:54Z
dc.descriptionComputational 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.
dc.identifierhttps://arxiv.org/abs/0905.2200
dc.identifierhttp://arxiv.org/abs/0905.2200
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/230324
dc.subjectDistributed, Parallel, and Cluster Computing
dc.subjectDatabases
dc.titleTowards Chip-on-Chip Neuroscience: Fast Mining of Frequent Episodes Using Graphics Processors
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