About NetPyNE

What is NetPyNE ?

Overview: NetPyNE is an NIH-funded tool for data-driven multiscale modeling of brain circuits. It enables users to consolidate complex experimental data from different brain scales into a unified mechanistic computational model. NetPyNE builds on top of NEURON, one of the most widely used neural simulation engines. NetPyNE is unique integrating all major steps of the modeling workflow under a single framework. The core of NetPyNE consists of a standardized JSON-like declarative language that allows the user to define the model across scales, from molecules to neurons to circuits. The NetPyNE API can then be used to automatically generate the corresponding NEURON network, run parallel simulations, optimize and explore parameters, and visualize and analyze the results through a wide range of built-in functions.

Graphical Web Application: All functionality is also available via a state-of-the art web-based graphical application, which now includes management of simulations and automated exploration of parameters. This is the only graphical tool that allows users to define parameters values to explore, run the corresponding simulations and visualize the results. Additionally, the web app is fully integrated with the Open Source Brain (OSB) platform, providing users with an online persistent workspace, file management, access to online resources and interactive jupyter notebooks. 

Interface/integration with other tools and standards: NetPyNE facilitates model sharing by exporting/importing to the NeuroML and SONATA standardized formats. It has been interfaced with CoreNEURON, and several large-scale models were benchmarked on GPUs for the first time, obtaining impressive 40x speedups. The interface with the LFPykit tool allows NetPyNE to generate dipole current moments for any arbitrary model, and simulate EEG signals at electrodes placed along a head volume conduction model. Similiar EEG/MEG measures can also be obtained through an interface with the Human Neocortical Neurosolver (HNN) tool. The new co-simulation interface between NetPyNE and The Virtual Brain (TVB) achieves a new milestone for multiscale modeling: linking molecular chemical signaling (via RxD) to whole-brain network dynamics. NetPyNE is now also available as an official service on the Human Brain Project EBRAINS platform (https://www.ebrains.eu/tools/netpyne). The SciUnit tool has been adapted to work with NetPyNE, resulting in the NetPyNEUnit package which facilitates model reproducibility, validation and evaluation. 

User community: At least 25 publications describe models or tools that have made use of NetPyNE, including our recent detailed models of the motor, auditory and somatosensory thalamocortical circuits, and of spinal cord circuits. Others have developed NetPyNE models to study Parkinson's disease, schizophrenia, ischemic stroke and epilepsy.

For more information please visit:

Overview of NetPyNE components and workflow.
Who is behind NetPyNE ?

Grant Funding:

  • National Institutes of Health (NIH), National Insititute of Biomedical Imaging and Bioengineering (NIBIB) U24 EB028998: “Dissemination of a tool for data-driven multiscale modeling of brain circuits”, Period: 2019-2024; Amount: $1,171,482; PI: Salvador Dura-Bernal.
  • NIH U24 U24 EB028998-04S1 (Admin Supp). "Development of robust cloud-based software for co-simulation of biophysical circuit and whole-brain network models.", Period: 2022-2023; Amount: $221,229; PI: Salvador Dura-Bernal.
  • NIH NIBIB U24 EB028998-05S1 (Admin Supp). "Exploring cloud GPUs to accelerate multiscale simulations of brain circuits using NetPyNE.", Period: 2023-2024; Amount: $212,072; PI: Salvador Dura-Bernal.

Research Grants making use of NetPyNE: 

  • Over 10 research grants currently make use of the tool (see link for details).

Current Core Developers:

  • Valery Bragin (Research Scientist, SUNY Downstate and Charite Berlin)
  • James Chen (Postdoctoral Reseaercher, SUNY Downstate)
  • Eugenio Urdapilleta (Visiting Researcher, SUNY Downstate; Researcher, Instituto Balseiro)
  • MetaCell LLC (Consultant Software Development Company, http://metacell.us)
  • Salvador Dura-Bernal (Assistant Professor / PI, SUNY Downstate)

Previous Core Developers:

  • Joe W Graham (Research Scientist, SUNY Downstate)

Major Contributors:

  • Padraig Gleeson (Principal Research Fellow, UCL)
  • William W Lytton (Distinguished Professor, SUNY)
  • Erica Y Griffith (MD/PhD Student, SUNY Downstate)
  • Craig Kelley (Postdoc, Columbia)
  • John Carre (Intern Student, SUNY Downstate)
  • David Kedziora (Postdoc, Sydney University)
  • Cliff C Kerr (Senior Research Scientist, Institute for Disease Modeling)
  • Siddartha Mitra (Graduate Student, SUNY Downstate)
  • Adam Newton (Postdoc, SUNY Downstate)

Governance Structure:

Major decisions about NetPyNE are made by the steering committee, guided by the Project roadmap and the Code of conduct. The committee includes members from a diverse range of institutions, positions and backgrounds. The current steering committee consists of the following members (in alphabetical order):

  • Salvador Dura-Bernal (Assistant Professor, State University of New York Downstate; Research Scientist, Nathan Kline Institute for Psychiatric Research)
  • Padraig Gleeson (Principal Research Fellow, University College London)
  • Joe W. Graham (Research Scientist, State University of New York Downstate)
  • Erica Y. Griffith (Graduate Student, State University of New York Downstate)
  • Michael Hines (Senior Research Scientist, Yale University)
  • Cliff C. Kerr (Senior Research Scientist, Institute for Disease Modeling)
  • William W. Lytton (Distinguished Professor, State University of New York Downstate; Kings County Hospital)
  • Robert A. McDougal (Assistant Professor, Yale University)
  • Samuel A. Neymotin (Research Scientist, Nathan Kline Institute for Psychiatric Research)
  • Benjamin A. Suter (Postdoctoral Fellow, Institute of Science and Technology Austria)
  • Subhashini Sivagnanam (Principal Computational and Data Science Research Specialist, San Diego Supercomputing Center
How to contribute ?

Questions, Suggestions, Contributions

Two kinds of contributions are solicited: 1. direct contributions to NetPyNE code described below. 2. Ancillary code contributions.

NetPyNE is open-source and available at https://github.com/suny-downstate-medical-center 

For questions or suggestions please use the NetPyNE forum, the NEURON+NetPyNE forum or submit an issue to GitHub.

Contributions are gladly welcome! Please fork the repository and make a Pull Request with your changes. See our Contributors Guide for more details.

Code of Conduct

This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.

We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.

Please read the full Code of Conduct.

Project roadmap

The current project roadmap encompasses four major categories: quality control, GUI extension, dissemination and community engagement, and development of new features. This roadmap was initially established under a primary 5-year NIH grant and has since been extended due to additional funding and continued progress. Below are the main objectives and estimated timelines:

Quality control: robustness, reliability and reproducibility

  • 2019-2021: Reliability - Test existing features, particularly recently added ones (RxD, subcellular connectivity, distributed saving, parameter optimization) such that they perform their intended function under all valid conditions and inputs.
  • 2020-2022: Robustness and error handling - Ensure the tool is able to cope with erroneous inputs and errors during execution. Improved tool robustness will include input validation, exception handling and informational messages.
  • 2022-2024: Reproducibility - Ensure simulation results are reproducible across the most common platforms, including different versions of operating systems, Python, NEURON, MPI library; and HPC platform setup (eg XSEDE/NSG, Google Cloud Platform).

GUI extension:

Extension of the graphical user interface (GUI), essential to engage new users and make the tool accessible to experimentalists, clinicians and students.

  • 2019-2020: Web-based multi-user deployment - Will allows users to build models and run simulations through a web browser over the internet, making the tool publicly available to the global research community.
  • 2019-2022: Incorporating missing components - Currently only accessible programmatically: RxD, subcellular connectivity, complex stimulation and parameter optimization (only grid search).
  • 2021-2023: Dynamic interactive plots - Improving plots by replacing the current static images with modern interactive and dynamic plots that facilitate understanding of complex and large datasets.
  • 2022-2026: Visualization of large networks - Improving performance to enable 3D visualization and manipulation of large-scale networks of detailed neurons (currently limited to a few hundred neurons).
  • 2021-2028: Integration with Open Source Brain and EBRAINS platforms.

Dissemination and community engagement:

We will implement complementary dissemination and engagement strategies to train and attract users and developers:

  • 2019-2020: Online documentation - Updated and comprehensive online documentation covering all the tool components, options and modes of usage, with examples, so both beginner and advanced users can fully exploit the tool.
  • 2020-2022: Online interactive tutorials - Will enable new users to receive training at their own pace through multimedia-rich step-by-step instructions that can be executed interactively (e.g. via GUI or Jupyter Notebook).
  • 2019-2028: Workshops/tutorials - Organized at neuroscience conferences to engage potential users by providing an overview of the tool functionalities and benefits.
  • 2020-2028: Annual 3-day in-person course - Will provide in-depth training to researchers/clinicians who could then teach tool usage at their labs or institutions.
  • 2020-2028: Annual Hackathon - Will train and engage developers, promoting long-term, sustainable, collaborative development.

Development of new features:

  • 2020-2026: Macroscopic scale modeling - Extend the framework to support macroscale data (e.g. EEG, MEG, BOLD-fMRI) and models (e.g. mean field models), thus linking this scale to the underlying circuit, cellular and molecular mechanisms.
  • 2021-2027: Machine learning analysis methods - Incorporate ML methods (e.g. clustering, dimensionality reduction, and deep learning) to explore and optimize large parameter spaces and analyze neural data.
  • 2022-2028: Reverse engineering of networks - Infer high-level compact network connectivity rules (generative model) from the full connection information of biological network models, using statistical (e.g. Bayesian inference) and graph theoretical analysis.
  • 2024-2028: AI-based automated model building - Extract and data mine available online datasets across scales, to populate a knowledge graph and automatically construct multiscale brain circuit models.