# The effects of sleep on neuronal coding in cortical layers and behavioral performance

> **NIH NIH F31** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2020 · $7,225

## Abstract

Project Summary/Abstract
Sleep is vital for optimal cognitive function. Yet, we do not understand how it influences and regulates neuronal
assemblies in the brain. Studies over the past several decades have presented compelling evidence that rest is
correlated with subsequent improved cognitive performance. For instance, people who rest or take a nap after
performing a new task remember it better and exhibit an elevated performance compared to those who do not
rest. Despite the prevalence and impact of sleep on behavioral performance, little is known about the neural
mechanisms of this improvement. Specifically, what is the impact of rest on sensory coding by individual neurons
and networks, and how does post-rest neuronal coding accuracy influence behavioral performance? We will
address this issue by examining whether brief sleep (20 min of rest) influences visual perceptual performance
and the coding of information across visual cortical layers. To accomplish this goal, we will use multiple-electrode
recording of single-unit activity and local field potentials (LFPs) in macaque mid-level visual cortex (area V4)
while the animals will perform a discrimination task before and after rest. Aim 1 will examine whether rest
improves stimulus coding in single neurons of each V4 cortical layer. Measures related to coding, sensitivity of
single neurons and the degree of synchrony between neuronal responses and LFPs, will be examined. Aim 2
will examine whether rest improves network coding in a layer-specific manner by measuring whether rest (i)
decreases correlated activity across the network, (ii) diminishes the level of synchrony in the population
response, and (iii) increases the amount of information in population activity. Aim 3 will examine whether rest
improves behavioral performance and whether rest influences the relationship between layer-dependent
neuronal coding and behavioral performance by examining (i) whether behavioral performance is improved after
rest, (ii) whether the post-rest network performance is correlated with behavioral performance, (iii) whether
synchronous population activity during rest is correlated with desynchronized post-rest population activity and
with improved behavioral performance, and (iv) whether there is a relationship between the amount of rest and
the improvement in network and behavioral performance. Overall, this proposal will expand our understanding
of the neurobiology of sleep and of the neuronal coding driving perception and thus, provide future solutions to
ameliorate sleep disorder and its detrimental effects on cognitive performance.

## Key facts

- **NIH application ID:** 9868819
- **Project number:** 5F31EY029993-02
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Natasha Kharas
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $7,225
- **Award type:** 5
- **Project period:** 2019-04-01 → 2020-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9868819

## Citation

> US National Institutes of Health, RePORTER application 9868819, The effects of sleep on neuronal coding in cortical layers and behavioral performance (5F31EY029993-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9868819. Licensed CC0.

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