# Robust modeling of within- and across-area population dynamics using recurrent neural networks

> **NIH NIH RF1** · EMORY UNIVERSITY · 2021 · $1,312,492

## Abstract

Over the past several decades, the ability to record from large populations of neurons (e.g., multi-electrode
arrays, neuropixels, calcium imaging) has increased exponentially, promising new avenues for understanding
the brain. These data have the promise to provide a qualitatively different view of activity within and across brain
areas than was previously possible, but the effort will require the development of advanced analytical tools. One
natural framework is provided by the tools of dynamical systems, which offer the means to uncover coordinated
time-varying activation patterns expressed across an interconnected network of recorded neurons, and to
characterize how these patterns relate to behavior. This framework has provided fundamental new insights into
information processing in these cortical circuits, including those underlying motor, sensory, and cognitive
processes. However, previous analytical approaches to uncovering dynamics have typically been developed and
tested in specific brain areas, for limited behaviors, in restricted behavioral settings. Ironically, it is not unusual
for these methods to have 10^5 parameters that need to be set or learned, and require careful tuning to properly
function. Yet the brain is not homogenous, and it is unclear how well these approaches can be made to
generalize to a variety of brain areas and behaviors, let alone by researchers who are not intimately familiar with
the methods. Further, assuming that the brain's dynamics stem from independent, isolated areas is a vast
oversimplification. Clearly, perceptual, cognitive, and motor functions all rely on activity distributed across
multiple, interacting brain areas, each of which likely has distinct dynamics. Communication between areas is a
dynamic process that underlies flexible function. There is growing recognition that population dynamics are
specifically structured to support inter-area interaction, and an immediate need for methods to accurately
uncover dynamics between interacting areas.
 We will address the challenge of generalized applicability to diverse brain areas by developing a powerful
new open-source toolkit for automated discovery of neural population dynamics, within highly divergent brain
areas. Further, we will extend this toolkit with new neural network architectures to model the dynamics between
interacting areas. Our approach, the Dynamical Systems ID toolkit (DSID), will support accurate and
straightforward application to data from different brain areas and behaviors without requiring great expertise or
infrastructure setup. DSID will leverage sequential autoencoders (SAEs), powerful and flexible deep learning
architectures that use recurrent neural networks to characterize nonlinear dynamical systems. We will validate
the generalizability of DSID using a combination of previously-collected and new multi-electrode recording data
from monkeys, including motor, sensory, and cognitive areas of cortex. Following their development...

## Key facts

- **NIH application ID:** 10263644
- **Project number:** 1RF1DA055667-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** Lee Miller
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,312,492
- **Award type:** 1
- **Project period:** 2021-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10263644

## Citation

> US National Institutes of Health, RePORTER application 10263644, Robust modeling of within- and across-area population dynamics using recurrent neural networks (1RF1DA055667-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10263644. Licensed CC0.

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