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