# Development of robust cloud-based software for co-simulation of biophysical circuit and whole-brain network models

> **NIH NIH U24** · SUNY DOWNSTATE MEDICAL CENTER · 2022 · $221,229

## Abstract

Project Summary
Experiments aimed at discovering how the brain works generate vast amounts of data that span multiple scales: from
interactions between individual molecules to waves of electrical activity across the entire brain. Computational
modeling provides a way to integrate and make sense of these data. Through the parent grant U24EB028998 we are
developing and disseminating NetPyNE, a tool for data-driven multiscale modeling of brain circuits. This tool provides
both a programmatic and graphical high-level interface to the widely-used NEURON simulator that facilitates the
development, parallel simulation, optimization and analysis of biophysically detailed neuronal circuits. NetPyNE is
unique compared to other neural circuit modeling tools (e.g. NEST, Brian) in incorporating NEURON's molecular
reaction-diffusion (RxD) module, which allows for detailed simulation of intracellular and extracellular chemical
signaling linked to electrophysiology. Significant progress has been made towards achieving the parent grant goal of
transforming NetPyNE into a solid and well-tested tool with a fully-featured GUI, and widely disseminating the tool
among the scientific community. This is evidenced through a growing user base -- the tool has been used to develop
at least 97 models from over 40 institutions worldwide, and has contributed to over 30 peer-reviewed publications.
NetPyNE has also been integrated or interfaced with multiple standards, tools and platforms in the community
including the NeuroML and SONATA, the Open Source Brain, EBRAINS and The Neuroscience Gateway (NSG),
HNN, SciUnit/SciDash, LFPy and coreNEURON tools.
The goal of this supplement is to enhance NetPyNE's interoperability by transforming a proof-of-concept interface
between NetPyNE and The Virtual Brain (TVB) into a robust, user-friendly, scalable and efficient software that is
portable across three cloud environments (EBRAINS, The Neuroscience Gateway and Google Cloud, via NIH
STRIDES). TVB is the worldwide reference tool for simulating macroscale whole-brain network models derived from
multi modal MRI (anatomical, functional and diffusion) and EEG datasets. The TVB-NetPyNE interface therefore
achieves a new milestone for multiscale modeling: linking molecular chemical signaling to whole-brain network
dynamics. Through this supplement we will also increase user adoption and community engagement of both NetPyNE
and the TVB-NetPyNE interface through 1) documentation, tutorials and example workflows; 2) dissemination and
training via workshops and courses; and 3) following software engineering and sharing best practices. This project
broadens the potential user base of NetPyNE by attracting new users from the TVB and cloud platform communities,
and more generally, clinicians and researchers working with MRI, EEG and MEG data. TVB has been downloaded
over 38,000 times, and has been used to construct and simulate over 1000 individual, connectome-based brain
network models and contribu...

## Key facts

- **NIH application ID:** 10609244
- **Project number:** 3U24EB028998-04S1
- **Recipient organization:** SUNY DOWNSTATE MEDICAL CENTER
- **Principal Investigator:** Salvador Dura-Bernal
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $221,229
- **Award type:** 3
- **Project period:** 2019-09-18 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10609244

## Citation

> US National Institutes of Health, RePORTER application 10609244, Development of robust cloud-based software for co-simulation of biophysical circuit and whole-brain network models (3U24EB028998-04S1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10609244. Licensed CC0.

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