# Dissemination of the Human Neocortical Neurosolver (HNN) software for circuit level interpretation of human MEG/EEG

> **NIH NIH U24** · BROWN UNIVERSITY · 2024 · $655,241

## Abstract

HNN U24 DISSEMINATION PROJECT SUMMARY
The Human Neocortical Neurosolver (HNN) neural modeling tool was developed with BRAIN Initiative funding
(R01EB022889: 09/2016–06/2020) to meet the Initiative’s goal to “develop innovative technologies to understand
brain circuits and ensembles of circuits that inform understanding of the human brain and mechanisms for
treating its disorder”. HNN is a biophysically principled neocortical circuit model with appropriate physics that
allow bridging from macroscale human magneto- and electro-encephalography (M/EEG) signals to their cellular
and circuit level generators. HNN is a hypothesis development and testing tool whose design and capabilities
are unique compared to other M/EEG neural modeling software. A key value of HNN is to connect functionally-
relevant human signals to circuit level dynamics studied in animal models, including data from revolutionary
genetic and imaging tools used in mice and monkeys (e.g., Neuropixel recordings, calcium, and voltage imaging).
These links are essential to discovering new principles of brain information processing and to developing
treatments when this processing is disrupted by neuropathology. There is widespread use of M/EEG, and myriad
inferences drawn from these signals about human brain function and health: HNN provides a highly accessible
tool for researchers to make principled connections to the detailed neurons and circuits underlying these signals,
to test new ideas and ground conclusions in circuit-level reality.
The neuroscience community is actively engaged in the use of HNN for basic and clinical research, including
studies of Alzheimer’s disease, autism spectrum disorder, pain, depression, and healthy development. While
HNN was successfully developed, there remain several challenges for growth and long-term sustainability. The
goal of this proposal is to support dissemination of HNN for broadly accessibly use and community-driven
development. Identified challenges in maintenance of HNN’s code and documentation that ensure Findability,
Accessibility, Interoperability, and Reusability will be addressed (Aim I), and key enhancements necessary to
support end-user needs and experimental validation of model-derived predictions will be developed (Aim II).
Interpreting the complex multiscale origin of M/EEG signals with HNN’s large-scale neural often requires domain
expertise in computational neural modeling, human M/EEG, and neural dynamics. To support this need, we will
continue to work with the community to integrate HNN into their projects through workshops, direct collaboration,
and consultation with end-user groups, and by enabling HNN simulation on freely accessible supercomputers
(Aim III). End-user feedback and documented support needs will be used to develop a Plan for Sustainability. A
world-class Steering Committee includes developers of widely-adopted neuroscience software who will share
their expertise to help HNN reach its maximal potential as transl...

## Key facts

- **NIH application ID:** 10909289
- **Project number:** 5U24NS129945-02
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** STEPHANIE Ruggiano JONES
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $655,241
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10909289

## Citation

> US National Institutes of Health, RePORTER application 10909289, Dissemination of the Human Neocortical Neurosolver (HNN) software for circuit level interpretation of human MEG/EEG (5U24NS129945-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10909289. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*