---
_id: '7405'
abstract:
- lang: eng
text: 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, for example 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.
article_number: e44494
article_processing_charge: No
article_type: original
author:
- first_name: Salvador
full_name: Dura-Bernal, Salvador
last_name: Dura-Bernal
- first_name: Benjamin
full_name: Suter, Benjamin
id: 4952F31E-F248-11E8-B48F-1D18A9856A87
last_name: Suter
orcid: 0000-0002-9885-6936
- first_name: Padraig
full_name: Gleeson, Padraig
last_name: Gleeson
- first_name: Matteo
full_name: Cantarelli, Matteo
last_name: Cantarelli
- first_name: Adrian
full_name: Quintana, Adrian
last_name: Quintana
- first_name: Facundo
full_name: Rodriguez, Facundo
last_name: Rodriguez
- first_name: David J
full_name: Kedziora, David J
last_name: Kedziora
- first_name: George L
full_name: Chadderdon, George L
last_name: Chadderdon
- first_name: Cliff C
full_name: Kerr, Cliff C
last_name: Kerr
- first_name: Samuel A
full_name: Neymotin, Samuel A
last_name: Neymotin
- first_name: Robert A
full_name: McDougal, Robert A
last_name: McDougal
- first_name: Michael
full_name: Hines, Michael
last_name: Hines
- first_name: Gordon MG
full_name: Shepherd, Gordon MG
last_name: Shepherd
- first_name: William W
full_name: Lytton, William W
last_name: Lytton
citation:
ama: Dura-Bernal S, Suter B, Gleeson P, et al. NetPyNE, a tool for data-driven multiscale
modeling of brain circuits. eLife. 2019;8. doi:10.7554/elife.44494
apa: Dura-Bernal, S., Suter, B., Gleeson, P., Cantarelli, M., Quintana, A., Rodriguez,
F., … Lytton, W. W. (2019). NetPyNE, a tool for data-driven multiscale modeling
of brain circuits. ELife. eLife Sciences Publications. https://doi.org/10.7554/elife.44494
chicago: Dura-Bernal, Salvador, Benjamin Suter, Padraig Gleeson, Matteo Cantarelli,
Adrian Quintana, Facundo Rodriguez, David J Kedziora, et al. “NetPyNE, a Tool
for Data-Driven Multiscale Modeling of Brain Circuits.” ELife. eLife Sciences
Publications, 2019. https://doi.org/10.7554/elife.44494.
ieee: S. Dura-Bernal et al., “NetPyNE, a tool for data-driven multiscale
modeling of brain circuits,” eLife, vol. 8. eLife Sciences Publications,
2019.
ista: Dura-Bernal S, Suter B, Gleeson P, Cantarelli M, Quintana A, Rodriguez F,
Kedziora DJ, Chadderdon GL, Kerr CC, Neymotin SA, McDougal RA, Hines M, Shepherd
GM, Lytton WW. 2019. NetPyNE, a tool for data-driven multiscale modeling of brain
circuits. eLife. 8, e44494.
mla: Dura-Bernal, Salvador, et al. “NetPyNE, a Tool for Data-Driven Multiscale Modeling
of Brain Circuits.” ELife, vol. 8, e44494, eLife Sciences Publications,
2019, doi:10.7554/elife.44494.
short: S. Dura-Bernal, B. Suter, P. Gleeson, M. Cantarelli, A. Quintana, F. Rodriguez,
D.J. Kedziora, G.L. Chadderdon, C.C. Kerr, S.A. Neymotin, R.A. McDougal, M. Hines,
G.M. Shepherd, W.W. Lytton, ELife 8 (2019).
date_created: 2020-01-30T09:08:01Z
date_published: 2019-05-31T00:00:00Z
date_updated: 2023-09-07T14:27:52Z
day: '31'
ddc:
- '570'
department:
- _id: PeJo
doi: 10.7554/elife.44494
external_id:
isi:
- '000468968400001'
pmid:
- '31025934'
file:
- access_level: open_access
checksum: 7014189c11c10a12feeeae37f054871d
content_type: application/pdf
creator: dernst
date_created: 2020-02-04T08:41:47Z
date_updated: 2020-07-14T12:47:57Z
file_id: '7444'
file_name: 2019_eLife_DuraBernal.pdf
file_size: 6182359
relation: main_file
file_date_updated: 2020-07-14T12:47:57Z
has_accepted_license: '1'
intvolume: ' 8'
isi: 1
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '05'
oa: 1
oa_version: Published Version
pmid: 1
publication: eLife
publication_identifier:
issn:
- 2050-084X
publication_status: published
publisher: eLife Sciences Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: NetPyNE, a tool for data-driven multiscale modeling of brain circuits
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 8
year: '2019'
...