# CRCNS: Unsupervised Learning of Hippocampal Sequence Dynamic in Sleep

> **NIH NIH R01** · RICE UNIVERSITY · 2021 · $335,515

## Abstract

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

## Key facts

- **NIH application ID:** 10191062
- **Project number:** 5R01NS115233-03
- **Recipient organization:** RICE UNIVERSITY
- **Principal Investigator:** KAMRAN DIBA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $335,515
- **Award type:** 5
- **Project period:** 2019-08-15 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10191062

## Citation

> US National Institutes of Health, RePORTER application 10191062, CRCNS: Unsupervised Learning of Hippocampal Sequence Dynamic in Sleep (5R01NS115233-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10191062. Licensed CC0.

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