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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, the authors present a solution that uses a massively parallel Graphics Processing Unit (GPU) to mine the stream of spikes. The authors focus on mining frequent episodes that capture coordinated events across time even in the presence of intervening background events. The contributions include new computation-to-core mapping schemes and novel strategies to map finite state machine-based counting algorithms onto the GPU.
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