NetPyNE, a tool for data-driven multiscale modeling of brain circuits

  1. Salvador Dura-Bernal  Is a corresponding author
  2. Benjamin A Suter
  3. Padraig Gleeson
  4. Matteo Cantarelli
  5. Adrian Quintana
  6. Facundo Rodriguez
  7. David J Kedziora
  8. George L Chadderdon
  9. Cliff C Kerr
  10. Samuel A Neymotin
  11. Robert A McDougal
  12. Michael Hines
  13. Gordon M G Shepherd
  14. William W Lytton
  1. State University of New York Downstate Medical Center, United States
  2. Northwestern University, United States
  3. University College London, United Kingdom
  4. Metacell LLC, United States
  5. EyeSeeTea Ltd, United Kingdom
  6. University of Sydney, Australia
  7. Yale University, United States

Abstract

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, e.g. connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.

Data availability

All data and models used in this work are publicly available from the following GitHub and ModelDB links:- Fig 3: https://github.com/Neurosim-lab/netpyne/tree/paper/examples/paper/fig3- Fig 5: https://github.com/Neurosim-lab/netpyne/tree/paper/examples/paper/fig5- Fig 6: https://github.com/Neurosim-lab/netpyne/tree/paper/examples/paper/fig6- Fig 7: https://github.com/Neurosim-lab/netpyne/tree/paper/examples/paper/fig7- Fig 8: https://github.com/Neurosim-lab/netpyne/tree/paper/examples/paper/fig8- Fig 9A: https://github.com/ceciliaromaro/PD_in_NetPyNE- Fig 9B: https://github.com/rtekin/myKnoxRepo- Fig 9C: https://github.com/rodriguez-facundo/LASCON-project- Fig 9D: https://github.com/angietep/CA1-NetPyNE-modelTable 1:Dentate gyrus:- Original: https://modeldb.yale.edu/155568- NetPyNE: https://github.com/rodriguez-facundo/LASCON-projectCA1 microcircuits:- Original: https://modeldb.yale.edu/123815- NetPyNE: https://github.com/angietep/CA1-NetPyNE-modelEpilepsy in thalamocortex:- Original: https://modeldb.yale.edu/234233- NetPyNE: https://github.com/rodriguez-facundo/LASCON-projectEEG and MEG in cortex / HNN model:- Original: https://github.com/jonescompneurolab/hnn- NetPyNE: https://github.com/jonescompneurolab/hnn/tree/netpyneMotor cortex with RL:
- Original: https://modeldb.yale.edu/183014- NetPyNE: https://github.com/Neurosim-lab/netpyne/tree/development/examples/RL_armCortical microcircuits:- Original: https://github.com/OpenSourceBrain/PotjansDiesmann2014/tree/master/PyNEST- NetPyNE: https://github.com/ceciliaromaro/PD_in_NetPyNE

Article and author information

Author details

  1. Salvador Dura-Bernal

    Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, Brooklyn, United States
    For correspondence
    salvadordura@gmail.com
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8561-5324
  2. Benjamin A Suter

    Department of Physiology, Northwestern University, Chicago, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9885-6936
  3. Padraig Gleeson

    Department of Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5963-8576
  4. Matteo Cantarelli

    Metacell LLC, Boston, United States
    Competing interests
    Matteo Cantarelli, is affiliated with Metacell LLC. The author has no other competing interests to declare..
  5. Adrian Quintana

    EyeSeeTea Ltd, Cheltenham, United Kingdom
    Competing interests
    Adrian Quintana, is affiliated with EyeSeeTea Ltd. The author has no other competing interests to declare..
  6. Facundo Rodriguez

    Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, Brooklyn, United States
    Competing interests
    No competing interests declared.
  7. David J Kedziora

    Complex Systems Group, School of Physics, University of Sydney, Sydney, Australia
    Competing interests
    No competing interests declared.
  8. George L Chadderdon

    Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, Brooklyn, United States
    Competing interests
    No competing interests declared.
  9. Cliff C Kerr

    Complex Systems Group, School of Physics, University of Sydney, Sydney, Australia
    Competing interests
    No competing interests declared.
  10. Samuel A Neymotin

    Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, Brooklyn, United States
    Competing interests
    No competing interests declared.
  11. Robert A McDougal

    Department of Neuroscience, Yale University, New Haven, United States
    Competing interests
    No competing interests declared.
  12. Michael Hines

    Department of Neuroscience, Yale University, New Haven, United States
    Competing interests
    No competing interests declared.
  13. Gordon M G Shepherd

    Department of Physiology, Northwestern University, Chicago, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1455-8262
  14. William W Lytton

    Department of Physiology and Pharmacology, State University of New York Downstate Medical Center, Brooklyn, United States
    Competing interests
    No competing interests declared.

Funding

National Institute of Biomedical Imaging and Bioengineering (U01EB017695)

  • Salvador Dura-Bernal
  • Benjamin A Suter
  • Matteo Cantarelli
  • Adrian Quintana
  • Facundo Rodriguez
  • Samuel A Neymotin
  • Michael Hines
  • Gordon M G Shepherd
  • William W Lytton

New York State Department of Health (DOH01-C32250GG-3450000)

  • Salvador Dura-Bernal
  • Facundo Rodriguez
  • William W Lytton

Wellcome (101445)

  • Padraig Gleeson

National Institute of Biomedical Imaging and Bioengineering (2R01DC012947-06A1)

  • Samuel A Neymotin

National Institute of Biomedical Imaging and Bioengineering (R01EB022903)

  • Salvador Dura-Bernal
  • Michael Hines
  • William W Lytton

National Institute of Biomedical Imaging and Bioengineering (R01MH086638)

  • Robert A McDougal
  • Michael Hines
  • William W Lytton

Wellcome (212941)

  • Padraig Gleeson

National Institute of Biomedical Imaging and Bioengineering (3R01EB022889)

  • Salvador Dura-Bernal
  • Matteo Cantarelli
  • Adrian Quintana
  • Facundo Rodriguez
  • Samuel A Neymotin
  • Michael Hines

Australian Research Council (DE140101375)

  • David J Kedziora
  • Cliff C Kerr

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Upinder Singh Bhalla, Tata Institute of Fundamental Research, India

Version history

  1. Received: December 19, 2018
  2. Accepted: April 25, 2019
  3. Accepted Manuscript published: April 26, 2019 (version 1)
  4. Version of Record published: May 24, 2019 (version 2)
  5. Version of Record updated: May 31, 2019 (version 3)

Copyright

© 2019, Dura-Bernal et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 6,423
    views
  • 732
    downloads
  • 106
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Salvador Dura-Bernal
  2. Benjamin A Suter
  3. Padraig Gleeson
  4. Matteo Cantarelli
  5. Adrian Quintana
  6. Facundo Rodriguez
  7. David J Kedziora
  8. George L Chadderdon
  9. Cliff C Kerr
  10. Samuel A Neymotin
  11. Robert A McDougal
  12. Michael Hines
  13. Gordon M G Shepherd
  14. William W Lytton
(2019)
NetPyNE, a tool for data-driven multiscale modeling of brain circuits
eLife 8:e44494.
https://doi.org/10.7554/eLife.44494

Share this article

https://doi.org/10.7554/eLife.44494

Further reading

    1. Computational and Systems Biology
    Iván Plaza-Menacho
    Insight

    A study of two enzymes in the brain reveals new insights into how redox reactions regulate the activity of protein kinases.

    1. Computational and Systems Biology
    2. Epidemiology and Global Health
    Javier I Ottaviani, Virag Sagi-Kiss ... Gunter GC Kuhnle
    Research Article

    The chemical composition of foods is complex, variable, and dependent on many factors. This has a major impact on nutrition research as it foundationally affects our ability to adequately assess the actual intake of nutrients and other compounds. In spite of this, accurate data on nutrient intake are key for investigating the associations and causal relationships between intake, health, and disease risk at the service of developing evidence-based dietary guidance that enables improvements in population health. Here, we exemplify the importance of this challenge by investigating the impact of food content variability on nutrition research using three bioactives as model: flavan-3-ols, (–)-epicatechin, and nitrate. Our results show that common approaches aimed at addressing the high compositional variability of even the same foods impede the accurate assessment of nutrient intake generally. This suggests that the results of many nutrition studies using food composition data are potentially unreliable and carry greater limitations than commonly appreciated, consequently resulting in dietary recommendations with significant limitations and unreliable impact on public health. Thus, current challenges related to nutrient intake assessments need to be addressed and mitigated by the development of improved dietary assessment methods involving the use of nutritional biomarkers.