# Enhancing the Efficiency of Non-REM Sleep Temporal Dynamics to Improve Insight Learning

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $194,547

## Abstract

Project Summary/Abstract Although several human studies suggest that sleep facilitates insight
learning, the means by which this could occur is unknown. One hypothesis suggests that key
elements in the environment necessary for insight are encoded in pieces during waking,
then replayed during sleep, allowing the pieces to self assemble into insight. However, it is
not clear which memories are selected for reactivation and processed in sleep versus
allowed to be forgotten. We have developed a novel computational hypothesis called temporal
scaffolding, which can serve as a platform to shed light on both questions. This hypothesis
suggests that sleep replay should especially aid in gaining insight into temporal hidden patterns
due to the unique compressed dynamics of memory replay occurring during non-rapid-eye-
movement sleep (NREMS) and the learned sequences that are replayed in NREMS are those that
were accompanied by bursts of activation from the Locus Coeruleus (LC) during the learning
session, increasing the probability for replay. We developed a new task for animals that measures
whether sleep facilitates insight into a hidden temporal order. We will use this task in combination
with a measurements of activity from hundreds of neurons in the hippocampus simultaneously
while animals are learning the task and assess activity from task-relevant neurons while animals
train and assess activity from the same neurons again while they sleep, and once more when they
are tested following sleep. In Aim 1 we will correlate the number of task-relevant hippocampal
replay bursts during NREMS with measures of performance on the subsequent wake period that
indicate whether the animals have gained insight into the hidden temporal order. Preliminary data
show that a subset of unmanipulated animals can achieve insight into this task and that sleep
boosts this gain of insight. In Aim 2 we will use an optogenetic approach to stimulate or silence
LC neurons at critical choice points during initial task exposure and see whether such
manipulation alters the density or type of replay events in subsequent sleep and influences the
gain of insight. Sleep deprived and LC-silenced groups will serve as controls for the LC-stimulated
animals. In Aim 3, the data gathered from the first two Aims will be entered into a computational
model of hippocampal-neocortical networks to better estimate the parameters determining how
temporal scaffolding occurs, which, in turn, will inform future mechanistic studies. Positive results
in each aim will underscore the importance of memory replay in insight learning, the contribution
of temporal scaffolding to this learning, and a learning mechanism (LC activity) modulating it. It
will also provide a new handle by which to boost memory processing during sleep.

## Key facts

- **NIH application ID:** 9832669
- **Project number:** 5R21MH119020-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Gina R Poe
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $194,547
- **Award type:** 5
- **Project period:** 2018-12-06 → 2021-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9832669

## Citation

> US National Institutes of Health, RePORTER application 9832669, Enhancing the Efficiency of Non-REM Sleep Temporal Dynamics to Improve Insight Learning (5R21MH119020-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9832669. Licensed CC0.

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