Paper Info
Title
Towards chip-on-chip neuroscience: fast mining of neuronal spike streams using graphics hardware
Abstract
Computational neuroscience is being revolutionized with the advent of multi-electrode arrays that provide real-time, dynamic perspectives into brain function. Mining neuronal spike streams from these chips is critical to understand the firing patterns of neurons and gain insight into the underlying cellular activity. To address this need, we present a solution that uses a massively parallel graphics processing unit (GPU) to mine the stream of spikes. We focus on mining frequent episodes that capture coordinated events across time even in the presence of intervening background events. Our contributions include new computation-to-core mapping schemes and novel strategies to map finite state machine-based counting algorithms onto the GPU. Together, these contributions move us towards 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 chip (the GPU) mines it at a scale previously unachievable.
Year
DOI
Venue
2010
10.1145/1787275.1787277
Conf. Computing Frontiers
Keywords
Field
DocType
data mining,multi-electrode array,computational neuroscience,spike train data,fast mining,mining neuronal spike stream,firing pattern,brain function,dynamic perspective,towards chip-on-chip neuroscience,graphics hardware,finite state,background event,finite state machine,chip,real time
ChIP-on-chip,Computational neuroscience,Neuroscience,Graphics hardware,Spike train,Massively parallel,Computer science,Parallel computing,Real-time computing,Chip,Finite-state machine,Graphics processing unit
Conference
Citations 
PageRank 
References 
3
0.44
6
Authors
7
Name
Order
Citations
PageRank
Yong Cao16810.33
Debprakash Patnaik219114.89
Sean P. Ponce3735.54
Jeremy Archuleta41025.00
Patrick Butler517311.71
Wu-chun Feng62812232.50
Naren Ramakrishnan71913176.25