CRCNS: Unsupervised Learning of Hippocampal Sequence Dynamic in Sleep

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In unit recordings from large populations of neurons, fast compressed sequential firing of neurons during rest and early sleep have been found to replay patterns first observed in active awake experience. These remarkable patterns have sparked widespread interest in the scientific community and beyond. Sequence replay is now considered to play a critical role in the long-term stabilization and storage of mnemonically important information. However, despite the general acknowledgement of the importance of the sequential structure, very little is known about the null background against which replay is compared. Specifically, are apparently 'non-replaying' spike patterns, as seen in late sleep, just simply noise? Because replay is typically assessed by comparison against a fixed known template, most methods can only determine whether the resemblance to the template is more than what might be expected from random spike trains. But these methods cannot appraise whether other patterns remain in the nonsignificant events. Recently, the Diba and Kemere labs successfully collaborated to address precisely this issue. We developed methods based on hidden Markov models (HMMs) to uncover temporal structure in spike trains of neurons in an unsupervised template-free manner. In this proposal, we aim to further improve these methods and to evaluate the hidden structure of spike trains in hippocampal neuronal populations during sleep. In our second specific aim, we will use HMMs to determine both co-active ensemble ("contextual") and temporal patterns ("sequential") structure in hippocampal spike trains in both pre- and post-task sleep. In the third specific aim, we will probe the essence of sleep replay further, by exposing animals to multiple novel and familiar maze environments prior to long durations of sleep. In the fourth specific aim, we will perform closed-loop disruption of neuronal population patterns to examine the causal interplay and reverberation of these patterns from early to late sleep. In summary, our proposal is designed to provide strongest characterization to date of the structure of "noise" in replay events. RELEVANCE (See instructions): This study will provide an opening to evaluate the role of sleep in reorganizing information in the brain and help to identify critical time windows and neuronal activities during sleep which are particularly important for information storage and stabilization. Our assumptions and deductions about the nature and purpose of sleep implicitly inform all manner of public policy, from the durations of shifts for hospital and relief workers, to morning start times of public schools. Understanding the function and mechanisms of sleep H