Towards chip-on-chip neuroscience

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.