# A Comparative Framework for Modeling the Low-Dimensional Geometry of Neural Population States

> **NIH NIH R01** · GEORGIA INSTITUTE OF TECHNOLOGY · 2020 · $1,142,747

## Abstract

Project Summary
Advances in neural recording technology now provide access to neural activity at high temporal resolutions, from
many brain areas, and during complex and naturalistic behavior. Interpreting these types of high-dimensional
and unconstrained neural recordings is still a major challenge in neuroscience. The aim of this project is to
develop innovative methods for distilling high-dimensional neural activity patterns into simpler low-dimensional
formats that can be effectively compared across time, conditions, or even across species. Our team is uniquely
positioned to not only develop these novel methods, but also apply them to characterize changes in neural
systems across a wide range of clinically-relevant perturbations, including addiction, sensory manipulation, and
disease. In this project, theory, methods, and models will be developed for: 1) learning low-dimensional latent
space models that align many neural datasets onto a common reference frame for comparison, 2) comparing
datasets and testing the impact of a variety of perturbations (e.g. monocular deprivation, addiction and
withdrawal) on the shape or geometry of neural activity from its baseline state, and 3) investigating the role of
specific cell types and microcircuits on shaping population activity over time, during sleep, and in response to
certain classes of perturbations. This project will provide new tools and frameworks for comparing neural
datasets, leading to robust measures of disease, signatures of addiction, and other network-level reflections of
environment and behavior. Significance: As neural datasets continue to grow in size, new methods for analysis
are becoming of utmost importance in driving scientific understanding of the brain. The methods developed in
this proposal will identify new ways to learn network-level signatures that allow us to link and compare different
neural activity patterns. A robust ability to compare activity across time and animals will have wide reaching
impacts, and provide new tools to advance network-level understanding of disease. Innovation: This project will
leverage state-of-the-art approaches in high-dimensional statistics and geometry, which are simultaneously
advancing in the context of deep learning (DL) architectures, and to tackle challenges in neural coding. This
project represents a truly innovative combination of tools in machine learning and computational neuroscience
which will likely transfer knowledge in both directions: from machine learning to neuroscience and back. The
unique application of advanced mathematical tools in geometry and optimization to population-level analysis of
perturbations will be transformative, not only for neuroscience but also in the study of DL architectures.

## Key facts

- **NIH application ID:** 10007243
- **Project number:** 1R01EB029852-01
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Eva Dyer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,142,747
- **Award type:** 1
- **Project period:** 2020-09-16 → 2024-09-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10007243

## Citation

> US National Institutes of Health, RePORTER application 10007243, A Comparative Framework for Modeling the Low-Dimensional Geometry of Neural Population States (1R01EB029852-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10007243. Licensed CC0.

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