--- _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' ...