# Multiscale computational frameworks for integrating large-scale cortical dynamics, connectivity, and behavior

> **NIH NIH RF1** · PRINCETON UNIVERSITY · 2021 · $691,402

## Abstract

Project Summary/Abstract
A central problem in neuroscience is to understand how activity arises from neural circuits to drive animal
behaviors. Solving this problem requires integrating information from multiple experimental modalities and
organization levels of the nervous system. While modern neurotechnologies are generating high-resolution maps
of the brain-wide neural activity and anatomical connectivity, novel theoretical frameworks are urgently needed
to realize the full potential of these datasets. Most state-of-the-art methods for analyzing high-dimensional data
are based on detecting correlations in neural activity and do not provide links to the underlying anatomical
connectivity and circuit mechanisms. As a result, conclusions derived with these methods rarely generalize
across different behaviors and are hard to validate in perturbation experiments. In contrast, mechanistic theories,
which combine connectivity, activity, and function, have been highly successful in understanding function of small
neural circuits. Conditions under which insights from small circuits scale to large distributed circuits have not
been explored. Mechanistic theories informed by multiple data modalities are critically missing to guide
experiments probing global neural dynamics on the brain-wide scale.
The main goal of this proposal is to develop computational frameworks for modeling global neural dynamics,
which utilize anatomical connectivity and predict rich behavioral outputs on single trials. Our project will address
two complementary aims. First, we will take advantage of recently available datasets of high-resolution brain-
wide neural activity and anatomical connectivity to construct a multiscale model of functional dynamics across
the mouse cortex. Integrating measurements across multiple scales, from mesoscopic to near-cellular resolution,
we aim to reveal the effective degrees of freedom at each scale, which constrain global neural dynamics and
drive rich patterns of behavior. Second, we will leverage techniques from dynamical systems theory and artificial
recurrent neural networks to develop circuit reduction methods that infer interpretable low-dimensional circuit
mechanisms of cognitive computations from high-dimensional neural activity data. Rather than merely detecting
correlations, our method infers the structural connectivity of an equivalent low-dimensional circuit that fits
projections of high-dimensional neural activity data and implements the behavioral task. We will apply this
method to multi-area neural activity recordings from behaving animals to reveal distributed circuit mechanisms
of context-dependent decision making. The computational frameworks developed in this proposal can be
validated in perturbation experiments and extended to other nervous systems and behaviors.

## Key facts

- **NIH application ID:** 10840682
- **Project number:** 7RF1DA055666-02
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** Tatiana Engel
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $691,402
- **Award type:** 7
- **Project period:** 2023-05-12 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10840682

## Citation

> US National Institutes of Health, RePORTER application 10840682, Multiscale computational frameworks for integrating large-scale cortical dynamics, connectivity, and behavior (7RF1DA055666-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10840682. Licensed CC0.

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