# CRCNS: Closed-Loop Computational Neuroscience for Causally Dissecting Circuits

> **NIH NIH R01** · GEORGIA INSTITUTE OF TECHNOLOGY · 2022 · $322,589

## Abstract

Despite substantial progress characterizing neural responses, it is particularly challenging to determine
 causal interactions within recurrently connected circuits due to the confounding influence of the
 interconnections. This proposed project pioneers a nascent field of closed-loop computational
neuroscience that enables real-time feedback stimulation during experiments to decouple recurrently
connected elements and make stronger causal inferences about their interactions. Specifically, the
 contributions of this project will include: Aim 1) Using modern unsupervised machine learning methods to
fit latent state dynamical system models of population responses under closed-loop stimulation. The
 developed techniques will be used to clamp firing rate in genetically targeted inhibitory interneurons
 across S 1 cortical laminae in the mouse to map the causal effect of inhibitory cells on the sensory gain in
 excitatory cells. Aim 2) Merging and extending tools from network feedback control and causal inference
 to identify functional connections between network nodes using realistic experimental constraints. These
 techniques will be used to clamp firing rate in different S1 laminae of the mouse, using distributed
 perturbations to identify the functional connectivity between microcircuit layers during sensory stimulation.
 Aim 3) Developing a large-scale computational modeling environment to serve as an in si\ico testbed for
the community.
 Significance: The proposed project changes the de facto standard use of stimulation in experiments to
 leverage the full power of new recording and s.timulation technology for decoupling recurrently connected
 variables and making stronger causal inferences.
 Broader impacts: While the project uses rodent somatosensation as a model system, the results of this
 project will provide new techniques to study neurologic disorders involving disfunction of recurrent circuits
 (e.g., epilepsy, Parkinson's disease and depression). The open-source implementations will constitute
 critical algorithmic infrastructure for closed-loop stimulation experiments. This project will also result in the
 production of new trainees in an emerging new interdisciplinary field of closed-loop computational
neuroscience.
RELEVANCE (See instructions):
 The neural circuits that fail in many neurologic disorders (e.g., epilepsy, Parkinson's disease and
 depression) are difficult to study because they involve complex feedback loops. This project will develop
 algorithms that combine measurements and stimulation in real-time to provide powerful new tools to
 uncover the operating principles of these circuits and change their operation. Discovery in this area can
 help improve understanding of neurologic disorders and development of new stimulation therapies.

## Key facts

- **NIH application ID:** 10472482
- **Project number:** 5R01NS115327-04
- **Recipient organization:** GEORGIA INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Christopher John Rozell
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $322,589
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10472482

## Citation

> US National Institutes of Health, RePORTER application 10472482, CRCNS: Closed-Loop Computational Neuroscience for Causally Dissecting Circuits (5R01NS115327-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10472482. Licensed CC0.

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