Title: Clusterless methods for understanding internally generated hippocampal sequences

Millisecond-timescale patterns of neural activity are the substrate for the computations that underlie complex cognitive processes. In the hippocampus, for example, internally generated sequences of hippocampal place cell activity that occurred during a recent experience are often replayed in a time-compressed manner during a phenomenon called sharp wave-ripples (SWRs) that last 100-200 ms long. To develop a causal understanding of the relationship between these patterns and the learning and memory processes they support, we need tools to identify these sequences as they occur and manipulate targeted circuits based on their content. In this talk, I will first present a clusterless decoding approach that makes it possible to identify and classify the representational content of SWRs in real-time for content-dependent closed-loop manipulation. I will then illustrate how clusterless methods can also be a powerful data analysis tool with some examples from our recent work on the structural organization of these sequences.