# Extracting network-level homeostatic rules for sleep/wake states

> **NIH NIH F32** · WASHINGTON UNIVERSITY · 2024 · $73,408

## Abstract

Project Abstract
The question of why animals sleep has puzzled scientists for over a century. Contemporary research suggests
that sleep serves as a compensatory mechanism for brain plasticity, restoring the network to a homeostatic set-
point known as criticality. However, the precise mechanisms between sleep and neural plasticity are still subjects
of intense debate, and the information processing aspect of this problem hasn’t been addressed experimentally.
This proposal aims to address this gap by positing the hypothesis that experience-dependent plasticity in
neuronal circuitry progressively undermines indices of an optimal computational regime, and
proportionally increases the likelihood of sleep.
 I will adopt a two-pronged approach, leveraging the diverse expertise of my mentorship team. The first
objective is to develop a machine learning algorithm that predicts the mechanisms of neural criticality and its
homeostasis. This model will challenge existing machine learning approaches, which often lack explanatory
power, by extracting dynamical rules from existing datasets using cutting-edge modeling and optimization
techniques. The second objective involves empirical testing to ascertain whether a plasticity threshold exists at
the network level that triggers sleep. This will be conducted through continuous neuronal recordings in freely
behaving mice, coupled with methods to induce plastic change in the network.
 The third objective is to construct a biophysically grounded mathematical model to identify mechanisms
that stabilize network information processing in the face of plastic changes. This model will incorporate methods
from control theory and dynamical systems and will be validated against the rules derived in the first objective
and the data collected in the second objective. This interdisciplinary project is groundbreaking in its attempt to
offer a dynamical and mathematical explanation for why plasticity requires sleep. Furthermore, in contrast to
many other works that focus on either theory or experiment, here I will support my theoretical work with empirical
data.
 Overall, the project promises to make significant contributions to both theoretical and applied
neuroscience. It seeks to develop a predictive model for sleep/wake behavior based on fundamental rules and
to elucidate the functional mechanisms by which sleep serves the healthy brain. By directly testing a
dynamical/mathematical explanation of why plasticity requires sleep, the project stands to resolve a long-
standing question in neuroscience. Furthermore, uncovering these mechanisms will fill the important gap of
adaptation for a possible fundamental framework of brain dynamics.

## Key facts

- **NIH application ID:** 10947441
- **Project number:** 1F32NS138250-01
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Leandro Jonathan Fosque
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $73,408
- **Award type:** 1
- **Project period:** 2024-07-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10947441

## Citation

> US National Institutes of Health, RePORTER application 10947441, Extracting network-level homeostatic rules for sleep/wake states (1F32NS138250-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10947441. Licensed CC0.

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