Towards chip-on-chip neuroscience: fast mining of neuronal spike streams using graphics hardware

Authors:
Yong Cao;Debprakash Patnaik;Sean Ponce;Jeremy Archuleta;Patrick Butler;Wu-chun Feng;Naren Ramakrishnan
Affiliations:
Virginia Polytechnic Institute and State University, Blacksburg, VA, USA;Virginia Polytechnic Institute and State University, Blacksburg, VA, USA;Virginia Polytechnic Institute and State University, Blacksburg, VA, USA;Virginia Polytechnic Institute and State University, Blacksburg, VA, USA;Virginia Polytechnic Institute and State University, Blacksburg, VA, USA;Virginia Polytechnic Institute and State University, Blacksburg, VA, USA;Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
Venue:
Proceedings of the 7th ACM international conference on Computing frontiers
Year:
2010

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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 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.