Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Chip-on-Chip Neuroscience: Fast Mining of Frequent Episodes
  Using Graphics Processors   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.

17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph