# The Impact of Sleep on Network Coding and Perceptual Performance

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2022 · $459,529

## Abstract

PROJECT SUMMARY
A critical issue for understanding sleep function is whether and how it impacts the accuracy of neuronal
network computations to improve behavioral performance. Indeed, studies over the past several decades have
shown that even brief periods of rest are correlated with subsequent improved perceptual and cognitive
performance. However, despite the prevalence and beneficial impact of sleep on behavioral performance, little
is known about the neural mechanisms of this improvement. We propose new studies to explore unchartered
territory: we will explore the beneficial impact of sleep on information coding across cortical circuits and on
perceptual performance. We propose to use multiple-electrode recordings simultaneously in three cortical
areas, early and mid-level visual cortex (areas V1 and V4) and dorsolateral prefrontal cortex (area dlPFC) to
examine, for the first time, the dynamics and coding in neuronal populations before, during, and after sleep,
and their impact on behavioral performance. In Aim 1, we will investigate whether the low-frequency
synchronization of network activity ubiquitously observed during slow-wave sleep is associated with a post-
sleep reduction in the degree of synchronized fluctuations of population activity in visual and prefrontal cortex.
In Aim 2, we will use electrical stimulation during quiet wakefulness to emulate the restorative effects of sleep
in the absence of sleep, and causally test our hypothesis in Aim 1 that slow-wave population activity during
sleep causes a reduction in synchronized fluctuations in population activity after sleep. This Aim will provide
proof of concept for invasive stimulation procedures to improve perceptual performance in the absence of
sleep, and will set the stage for future noninvasive procedures in humans. In Aim 3, we will use optogenetic
stimulation to test the mechanism of the sleep-dependent improvement in neuronal and behavioral
performance. We hypothesize that sleep is associated with increased synaptic efficacy in local circuits that
persists during post-sleep wakefulness. We further hypothesize that this increase in synaptic efficacy elevates
firing rates and spike timing coordination between neurons after sleep to improve the accuracy of encoding
information in population activity. Our research has the potential to advance our understanding of the neural
mechanisms underlying sleep and thus provide future solutions to ameliorate the detrimental effects of sleep
disorder on cognitive performance, including practical applications for non-invasive neuronal prosthetic
devices.

## Key facts

- **NIH application ID:** 10392202
- **Project number:** 2R01EY026156-06
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** VALENTIN DRAGOI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $459,529
- **Award type:** 2
- **Project period:** 2016-08-01 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10392202

## Citation

> US National Institutes of Health, RePORTER application 10392202, The Impact of Sleep on Network Coding and Perceptual Performance (2R01EY026156-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10392202. Licensed CC0.

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