In the CA1 region of the rat hippocampus, fast field oscillations termed sharp wave ripples have been identified as playing a crucial role in memory formation and learning. During ripple activity, particular sequences of neurons fire in a phenomena called replay. So termed because the spiking encodes patterns of past experiences, the exact role of the content of replay is an active subject of investigation in order to determines its relationship with learning and memory guided decision making. A need arises for systems that can decode replay activity during ripples in real time. This necessitates fast algorithms for both spike sorting and ripple detection with the lowest possible latency. A low latency implementation makes possible feedback experiments where decoded ripple sequences can, with minimal delay, trigger stimulating pulses that can disrupt particular kinds of decoded information before they can contribute to behavior. In this study, we optimize and implement a recently proposed online spike sorting algorithm for an increasingly popular electrophysiological software suite and measure improvements that greatly enhance its multi-tetrode decoding capabilities. Synchronizing with online ripple detection, this novel framework will allows experimenters to study the effects of disrupting replay activity with a degree of granularity hitherto unavailable.
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