1 Temporal Data Mining for Neuroscience

Today, multi-electrode arrays (MEAs) capture neuronal spike streams in real time, thus providing dynamic perspectives into brain function. Mining such spike streams from these MEAs is critical towards understanding the firing patterns of neurons and gaining insight into the underlying cellular activity. However, the acquisition rate of neuronal data places a tremendous computational burden on the subsequent temporal data mining of these spike streams. Thus, computational neuroscience seeks innovative approaches towards tackling this problem and eventually solving it efficiently and in real time. In this chapter, we present a solution that uses graphics processing units (GPUs) to mine spike train datasets. Specifically, our solution delivers a novel mapping of a “finite state machine for data mining” onto the GPU while simultaneously addressing a wide range of neuronal input characteristics. This solution ultimately transforms the task of temporal data mining of spike trains from a batch-oriented process towards a real-time one.