In the rat hippocampus, fast oscillations termed sharp wave ripples and an associated sequential firing of neurons, termed replay, have been identified as playing a crucial role in memory formation and learning. The term 'replay' is used since the observed spiking encodes patterns of past experiences. To determine the role of replay in learning and decision making, a need arises for systems that can decode replay activity observed during ripples. This necessitates online algorithms for both spike sorting and ripple detection at low latencies. In my work, I have developed and tested an improved method for ripple detection and tested its performance against previous methods. Further, I have optimized a recently proposed spike sorting algorithm based on real-time bayesian inference so that it can run online in a multi-tetrode scenario, and implemented it, along with ripple detection, for the open-source electrophysiological suite, "open-ephys". The algorithm's parameters were also analyzed for their suitability in operating in an unsupervised scenario. These two modules are integrated to form a system uniquely suited to decoding neuronal sequences during sharp wave ripple events.