Parallel Mining of Neuronal Spike Streams on Graphics Processing Units

Abstract

Multi-electrode arrays (MEAs) provide dynamic and spatial perspectives into brain function by capturing the temporal behavior of spikes recorded from cultures and living tissue. Understanding the firing patterns of neurons implicit in these spike trains is crucial to gaining insight into cellular activity. We present a solution involving a massively parallel graphics processing unit (GPU) to mine spike train datasets. We focus on mining frequent episodes of firing patterns that capture coordinated events even in the presence of intervening background events. We present two algorithmic strategies—hybrid mining and two-pass elimination—to map the finite state machine-based counting algorithms onto GPUs. These strategies explore different computation-to-core mapping schemes and illustrate innovative parallel algorithm design patterns for temporal data mining. We also provide a multi-GPU mining framework, which exhibits additional performance enhancement. Together, these contributions move us towards a real-time solution to neuronal data mining.