[{"has_accepted_license":"1","citation":{"short":"J. Guzmán, A. Schlögl, C. Espinoza Martinez, X. Zhang, B. Suter, P.M. Jonas, (2021).","apa":"Guzmán, J., Schlögl, A., Espinoza Martinez, C., Zhang, X., Suter, B., &#38; Jonas, P. M. (2021). How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network. IST Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:10110\">https://doi.org/10.15479/AT:ISTA:10110</a>","chicago":"Guzmán, José, Alois Schlögl, Claudia  Espinoza Martinez, Xiaomin Zhang, Benjamin Suter, and Peter M Jonas. “How Connectivity Rules and Synaptic Properties Shape the Efficacy of Pattern Separation in the Entorhinal Cortex–Dentate Gyrus–CA3 Network.” IST Austria, 2021. <a href=\"https://doi.org/10.15479/AT:ISTA:10110\">https://doi.org/10.15479/AT:ISTA:10110</a>.","ista":"Guzmán J, Schlögl A, Espinoza Martinez C, Zhang X, Suter B, Jonas PM. 2021. How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network, IST Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:10110\">10.15479/AT:ISTA:10110</a>.","ama":"Guzmán J, Schlögl A, Espinoza Martinez C, Zhang X, Suter B, Jonas PM. How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network. 2021. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:10110\">10.15479/AT:ISTA:10110</a>","mla":"Guzmán, José, et al. <i>How Connectivity Rules and Synaptic Properties Shape the Efficacy of Pattern Separation in the Entorhinal Cortex–Dentate Gyrus–CA3 Network</i>. IST Austria, 2021, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:10110\">10.15479/AT:ISTA:10110</a>.","ieee":"J. Guzmán, A. Schlögl, C. Espinoza Martinez, X. Zhang, B. Suter, and P. M. Jonas, “How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network.” IST Austria, 2021."},"date_updated":"2024-03-25T23:30:07Z","abstract":[{"lang":"eng","text":"Pattern separation is a fundamental brain computation that converts small differences in input patterns into large differences in output patterns. Several synaptic mechanisms of pattern separation have been proposed, including code expansion, inhibition and plasticity; however, which of these mechanisms play a role in the entorhinal cortex (EC)–dentate gyrus (DG)–CA3 circuit, a classical pattern separation circuit, remains unclear. Here we show that a biologically realistic, full-scale EC–DG–CA3 circuit model, including granule cells (GCs) and parvalbumin-positive inhibitory interneurons (PV+-INs) in the DG, is an efficient pattern separator. Both external gamma-modulated inhibition and internal lateral inhibition mediated by PV+-INs substantially contributed to pattern separation. Both local connectivity and fast signaling at GC–PV+-IN synapses were important for maximum effectiveness. Similarly, mossy fiber synapses with conditional detonator properties contributed to pattern separation. By contrast, perforant path synapses with Hebbian synaptic plasticity and direct EC–CA3 connection shifted the network towards pattern completion. Our results demonstrate that the specific properties of cells and synapses optimize higher-order computations in biological networks and might be useful to improve the deep learning capabilities of technical networks."}],"type":"software","license":"https://opensource.org/licenses/GPL-3.0","_id":"10110","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","month":"12","date_published":"2021-12-16T00:00:00Z","ddc":["005"],"tmp":{"name":"GNU General Public License 3.0","legal_code_url":"https://www.gnu.org/licenses/gpl-3.0.en.html","short":"GPL 3.0"},"author":[{"full_name":"Guzmán, José","orcid":"0000-0003-2209-5242","last_name":"Guzmán","first_name":"José","id":"30CC5506-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Schlögl, Alois","first_name":"Alois","last_name":"Schlögl","orcid":"0000-0002-5621-8100","id":"45BF87EE-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Espinoza Martinez, Claudia ","last_name":"Espinoza Martinez","orcid":"0000-0003-4710-2082","first_name":"Claudia ","id":"31FFEE2E-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Zhang, Xiaomin","id":"423EC9C2-F248-11E8-B48F-1D18A9856A87","last_name":"Zhang","first_name":"Xiaomin"},{"full_name":"Suter, Benjamin","first_name":"Benjamin","orcid":"0000-0002-9885-6936","last_name":"Suter","id":"4952F31E-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Jonas, Peter M","first_name":"Peter M","last_name":"Jonas","orcid":"0000-0001-5001-4804","id":"353C1B58-F248-11E8-B48F-1D18A9856A87"}],"related_material":{"link":[{"description":"News on IST Webpage","relation":"press_release","url":"https://ist.ac.at/en/news/spot-the-difference/"}],"record":[{"relation":"used_for_analysis_in","id":"10816","status":"public"}]},"doi":"10.15479/AT:ISTA:10110","file_date_updated":"2021-10-08T08:46:04Z","day":"16","department":[{"_id":"PeJo"},{"_id":"ScienComp"}],"title":"How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network","oa":1,"file":[{"access_level":"open_access","file_name":"patternseparation-main (1).zip","success":1,"date_created":"2021-10-08T08:46:04Z","checksum":"f92f8931cad0aa7e411c1715337bf408","content_type":"application/x-zip-compressed","file_size":332990101,"date_updated":"2021-10-08T08:46:04Z","creator":"cchlebak","file_id":"10114","relation":"main_file"}],"publisher":"IST Austria","status":"public","date_created":"2021-10-08T06:44:22Z","year":"2021"},{"article_processing_charge":"No","publication":"Nature Computational Science","language":[{"iso":"eng"}],"year":"2021","acknowledged_ssus":[{"_id":"SSU"}],"acknowledgement":"We thank A. Aertsen, N. Kopell, W. Maass, A. Roth, F. Stella and T. Vogels for critically reading earlier versions of the manuscript. We are grateful to F. Marr and C. Altmutter for excellent technical assistance, E. Kralli-Beller for manuscript editing, and the Scientific Service Units of IST Austria for efficient support. Finally, we thank T. Carnevale, L. Erdös, M. Hines, D. Nykamp and D. Schröder for useful discussions, and R. Friedrich and S. Wiechert for sharing unpublished data. This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 692692, P.J.) and the Fond zur Förderung der Wissenschaftlichen Forschung (Z 312-B27, Wittgenstein award to P.J. and P 31815 to S.J.G.).","intvolume":"         1","main_file_link":[{"open_access":"1","url":"https://www.biorxiv.org/content/10.1101/647800"}],"title":"How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network","related_material":{"record":[{"relation":"software","id":"10110","status":"public"}],"link":[{"url":"https://ista.ac.at/en/news/spot-the-difference/","relation":"press_release"}]},"article_type":"original","doi":"10.1038/s43588-021-00157-1","ec_funded":1,"oa_version":"Submitted Version","file_date_updated":"2022-06-18T22:30:03Z","has_accepted_license":"1","date_updated":"2023-08-10T22:30:10Z","citation":{"apa":"Guzmán, J., Schlögl, A., Espinoza Martinez, C., Zhang, X., Suter, B., &#38; Jonas, P. M. (2021). How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network. <i>Nature Computational Science</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s43588-021-00157-1\">https://doi.org/10.1038/s43588-021-00157-1</a>","short":"J. Guzmán, A. Schlögl, C. Espinoza Martinez, X. Zhang, B. Suter, P.M. Jonas, Nature Computational Science 1 (2021) 830–842.","ista":"Guzmán J, Schlögl A, Espinoza Martinez C, Zhang X, Suter B, Jonas PM. 2021. How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network. Nature Computational Science. 1(12), 830–842.","chicago":"Guzmán, José, Alois Schlögl, Claudia  Espinoza Martinez, Xiaomin Zhang, Benjamin Suter, and Peter M Jonas. “How Connectivity Rules and Synaptic Properties Shape the Efficacy of Pattern Separation in the Entorhinal Cortex–Dentate Gyrus–CA3 Network.” <i>Nature Computational Science</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1038/s43588-021-00157-1\">https://doi.org/10.1038/s43588-021-00157-1</a>.","ama":"Guzmán J, Schlögl A, Espinoza Martinez C, Zhang X, Suter B, Jonas PM. How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network. <i>Nature Computational Science</i>. 2021;1(12):830-842. doi:<a href=\"https://doi.org/10.1038/s43588-021-00157-1\">10.1038/s43588-021-00157-1</a>","ieee":"J. Guzmán, A. Schlögl, C. Espinoza Martinez, X. Zhang, B. Suter, and P. M. Jonas, “How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network,” <i>Nature Computational Science</i>, vol. 1, no. 12. Springer Nature, pp. 830–842, 2021.","mla":"Guzmán, José, et al. “How Connectivity Rules and Synaptic Properties Shape the Efficacy of Pattern Separation in the Entorhinal Cortex–Dentate Gyrus–CA3 Network.” <i>Nature Computational Science</i>, vol. 1, no. 12, Springer Nature, 2021, pp. 830–42, doi:<a href=\"https://doi.org/10.1038/s43588-021-00157-1\">10.1038/s43588-021-00157-1</a>."},"type":"journal_article","date_published":"2021-12-16T00:00:00Z","month":"12","ddc":["610"],"scopus_import":"1","publisher":"Springer Nature","page":"830-842","file":[{"file_size":1699466,"content_type":"application/pdf","creator":"patrickd","date_updated":"2022-06-18T22:30:03Z","relation":"main_file","file_id":"11430","file_name":"Guzmanetal2021.pdf","embargo":"2022-06-17","access_level":"open_access","checksum":"9fec5b667909ef52be96d502e4f8c2ae","date_created":"2022-06-02T12:51:07Z"},{"title":"Supplementary Material","file_id":"11431","relation":"supplementary_material","date_updated":"2022-06-18T22:30:03Z","creator":"patrickd","content_type":"application/pdf","file_size":3005651,"date_created":"2022-06-02T12:53:47Z","checksum":"52a005b13a114e3c3a28fa6bbe8b1a8d","access_level":"open_access","embargo":"2022-06-17","file_name":"Guzmanetal2021Suppl.pdf"}],"status":"public","date_created":"2022-03-04T08:32:36Z","day":"16","department":[{"_id":"PeJo"}],"keyword":["general medicine"],"quality_controlled":"1","issue":"12","oa":1,"author":[{"full_name":"Guzmán, José","id":"30CC5506-F248-11E8-B48F-1D18A9856A87","last_name":"Guzmán","orcid":"0000-0003-2209-5242","first_name":"José"},{"full_name":"Schlögl, Alois","orcid":"0000-0002-5621-8100","last_name":"Schlögl","first_name":"Alois","id":"45BF87EE-F248-11E8-B48F-1D18A9856A87"},{"id":"31FFEE2E-F248-11E8-B48F-1D18A9856A87","first_name":"Claudia ","last_name":"Espinoza Martinez","orcid":"0000-0003-4710-2082","full_name":"Espinoza Martinez, Claudia "},{"full_name":"Zhang, Xiaomin","id":"423EC9C2-F248-11E8-B48F-1D18A9856A87","last_name":"Zhang","first_name":"Xiaomin"},{"first_name":"Benjamin","orcid":"0000-0002-9885-6936","last_name":"Suter","id":"4952F31E-F248-11E8-B48F-1D18A9856A87","full_name":"Suter, Benjamin"},{"id":"353C1B58-F248-11E8-B48F-1D18A9856A87","first_name":"Peter M","orcid":"0000-0001-5001-4804","last_name":"Jonas","full_name":"Jonas, Peter M"}],"project":[{"_id":"25B7EB9E-B435-11E9-9278-68D0E5697425","name":"Biophysics and circuit function of a giant cortical glumatergic synapse","grant_number":"692692","call_identifier":"H2020"},{"grant_number":"Z00312","call_identifier":"FWF","_id":"25C5A090-B435-11E9-9278-68D0E5697425","name":"The Wittgenstein Prize"}],"volume":1,"publication_identifier":{"issn":["2662-8457"]},"abstract":[{"lang":"eng","text":"Pattern separation is a fundamental brain computation that converts small differences in input patterns into large differences in output patterns. Several synaptic mechanisms of pattern separation have been proposed, including code expansion, inhibition and plasticity; however, which of these mechanisms play a role in the entorhinal cortex (EC)–dentate gyrus (DG)–CA3 circuit, a classical pattern separation circuit, remains unclear. Here we show that a biologically realistic, full-scale EC–DG–CA3 circuit model, including granule cells (GCs) and parvalbumin-positive inhibitory interneurons (PV+-INs) in the DG, is an efficient pattern separator. Both external gamma-modulated inhibition and internal lateral inhibition mediated by PV+-INs substantially contributed to pattern separation. Both local connectivity and fast signaling at GC–PV+-IN synapses were important for maximum effectiveness. Similarly, mossy fiber synapses with conditional detonator properties contributed to pattern separation. By contrast, perforant path synapses with Hebbian synaptic plasticity and direct EC–CA3 connection shifted the network towards pattern completion. Our results demonstrate that the specific properties of cells and synapses optimize higher-order computations in biological networks and might be useful to improve the deep learning capabilities of technical networks."}],"_id":"10816","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published"},{"department":[{"_id":"PeJo"}],"isi":1,"day":"01","issue":"6","oa":1,"quality_controlled":"1","publisher":"Springer Nature","file":[{"content_type":"application/pdf","file_size":38574802,"date_updated":"2021-12-02T23:30:05Z","creator":"cziletti","file_id":"9639","relation":"main_file","access_level":"open_access","embargo":"2021-12-01","file_name":"VandaeletalAuthorVersion2021.pdf","date_created":"2021-07-08T12:27:55Z","checksum":"7eb580abd8893cdb0b410cf41bc8c263"}],"page":"2947–2967","status":"public","date_created":"2021-05-30T22:01:24Z","_id":"9438","abstract":[{"text":"Rigorous investigation of synaptic transmission requires analysis of unitary synaptic events by simultaneous recording from presynaptic terminals and postsynaptic target neurons. However, this has been achieved at only a limited number of model synapses, including the squid giant synapse and the mammalian calyx of Held. Cortical presynaptic terminals have been largely inaccessible to direct presynaptic recording, due to their small size. Here, we describe a protocol for improved subcellular patch-clamp recording in rat and mouse brain slices, with the synapse in a largely intact environment. Slice preparation takes ~2 h, recording ~3 h and post hoc morphological analysis 2 d. Single presynaptic hippocampal mossy fiber terminals are stimulated minimally invasively in the bouton-attached configuration, in which the cytoplasmic content remains unperturbed, or in the whole-bouton configuration, in which the cytoplasmic composition can be precisely controlled. Paired pre–postsynaptic recordings can be integrated with biocytin labeling and morphological analysis, allowing correlative investigation of synapse structure and function. Paired recordings can be obtained from mossy fiber terminals in slices from both rats and mice, implying applicability to genetically modified synapses. Paired recordings can also be performed together with axon tract stimulation or optogenetic activation, allowing comparison of unitary and compound synaptic events in the same target cell. Finally, paired recordings can be combined with spontaneous event analysis, permitting collection of miniature events generated at a single identified synapse. In conclusion, the subcellular patch-clamp techniques detailed here should facilitate analysis of biophysics, plasticity and circuit function of cortical synapses in the mammalian central nervous system.","lang":"eng"}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","publication_status":"published","author":[{"full_name":"Vandael, David H","orcid":"0000-0001-7577-1676","last_name":"Vandael","first_name":"David H","id":"3AE48E0A-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Okamoto, Yuji","first_name":"Yuji","last_name":"Okamoto","orcid":"0000-0003-0408-6094","id":"3337E116-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0003-0005-401X","last_name":"Borges Merjane","first_name":"Carolina","id":"4305C450-F248-11E8-B48F-1D18A9856A87","full_name":"Borges Merjane, Carolina"},{"full_name":"Vargas Barroso, Victor M","last_name":"Vargas Barroso","first_name":"Victor M","id":"2F55A9DE-F248-11E8-B48F-1D18A9856A87"},{"id":"4952F31E-F248-11E8-B48F-1D18A9856A87","first_name":"Benjamin","orcid":"0000-0002-9885-6936","last_name":"Suter","full_name":"Suter, Benjamin"},{"first_name":"Peter M","orcid":"0000-0001-5001-4804","last_name":"Jonas","id":"353C1B58-F248-11E8-B48F-1D18A9856A87","full_name":"Jonas, Peter M"}],"external_id":{"isi":["000650528700003"],"pmid":["33990799"]},"volume":16,"publication_identifier":{"eissn":["17502799"],"issn":["17542189"]},"project":[{"name":"Biophysics and circuit function of a giant cortical glumatergic synapse","_id":"25B7EB9E-B435-11E9-9278-68D0E5697425","grant_number":"692692","call_identifier":"H2020"},{"name":"The Wittgenstein Prize","_id":"25C5A090-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"Z00312"},{"grant_number":"V00739","call_identifier":"FWF","name":"Structural plasticity at mossy fiber-CA3 synapses","_id":"2696E7FE-B435-11E9-9278-68D0E5697425"}],"acknowledgement":"This project received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 692692 to P.J.) and the Fond zur Förderung der Wissenschaftlichen Forschung (Z 312-B27, Wittgenstein award to P.J., V 739-B27 to C.B.M.). We are grateful to F. Marr and C. Altmutter for excellent technical assistance and cell reconstruction, E. Kralli-Beller for manuscript editing, and the Scientific Service Units of IST Austria, especially T. Asenov and Miba machine shop, for maximally efficient support.","intvolume":"        16","title":"Subcellular patch-clamp techniques for single-bouton stimulation and simultaneous pre- and postsynaptic recording at cortical synapses","publication":"Nature Protocols","article_processing_charge":"No","language":[{"iso":"eng"}],"year":"2021","acknowledged_ssus":[{"_id":"M-Shop"}],"has_accepted_license":"1","citation":{"ieee":"D. H. Vandael, Y. Okamoto, C. Borges Merjane, V. M. Vargas Barroso, B. Suter, and P. M. Jonas, “Subcellular patch-clamp techniques for single-bouton stimulation and simultaneous pre- and postsynaptic recording at cortical synapses,” <i>Nature Protocols</i>, vol. 16, no. 6. Springer Nature, pp. 2947–2967, 2021.","mla":"Vandael, David H., et al. “Subcellular Patch-Clamp Techniques for Single-Bouton Stimulation and Simultaneous Pre- and Postsynaptic Recording at Cortical Synapses.” <i>Nature Protocols</i>, vol. 16, no. 6, Springer Nature, 2021, pp. 2947–2967, doi:<a href=\"https://doi.org/10.1038/s41596-021-00526-0\">10.1038/s41596-021-00526-0</a>.","ama":"Vandael DH, Okamoto Y, Borges Merjane C, Vargas Barroso VM, Suter B, Jonas PM. Subcellular patch-clamp techniques for single-bouton stimulation and simultaneous pre- and postsynaptic recording at cortical synapses. <i>Nature Protocols</i>. 2021;16(6):2947–2967. doi:<a href=\"https://doi.org/10.1038/s41596-021-00526-0\">10.1038/s41596-021-00526-0</a>","ista":"Vandael DH, Okamoto Y, Borges Merjane C, Vargas Barroso VM, Suter B, Jonas PM. 2021. Subcellular patch-clamp techniques for single-bouton stimulation and simultaneous pre- and postsynaptic recording at cortical synapses. Nature Protocols. 16(6), 2947–2967.","chicago":"Vandael, David H, Yuji Okamoto, Carolina Borges Merjane, Victor M Vargas Barroso, Benjamin Suter, and Peter M Jonas. “Subcellular Patch-Clamp Techniques for Single-Bouton Stimulation and Simultaneous Pre- and Postsynaptic Recording at Cortical Synapses.” <i>Nature Protocols</i>. Springer Nature, 2021. <a href=\"https://doi.org/10.1038/s41596-021-00526-0\">https://doi.org/10.1038/s41596-021-00526-0</a>.","apa":"Vandael, D. H., Okamoto, Y., Borges Merjane, C., Vargas Barroso, V. M., Suter, B., &#38; Jonas, P. M. (2021). Subcellular patch-clamp techniques for single-bouton stimulation and simultaneous pre- and postsynaptic recording at cortical synapses. <i>Nature Protocols</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41596-021-00526-0\">https://doi.org/10.1038/s41596-021-00526-0</a>","short":"D.H. Vandael, Y. Okamoto, C. Borges Merjane, V.M. Vargas Barroso, B. Suter, P.M. Jonas, Nature Protocols 16 (2021) 2947–2967."},"date_updated":"2023-08-10T22:30:51Z","pmid":1,"type":"journal_article","scopus_import":"1","month":"06","date_published":"2021-06-01T00:00:00Z","ddc":["570"],"ec_funded":1,"doi":"10.1038/s41596-021-00526-0","article_type":"original","file_date_updated":"2021-12-02T23:30:05Z","oa_version":"Submitted Version"},{"author":[{"full_name":"Dura-Bernal, Salvador","last_name":"Dura-Bernal","first_name":"Salvador"},{"orcid":"0000-0002-9885-6936","last_name":"Suter","first_name":"Benjamin","id":"4952F31E-F248-11E8-B48F-1D18A9856A87","full_name":"Suter, Benjamin"},{"last_name":"Gleeson","first_name":"Padraig","full_name":"Gleeson, Padraig"},{"first_name":"Matteo","last_name":"Cantarelli","full_name":"Cantarelli, Matteo"},{"full_name":"Quintana, Adrian","last_name":"Quintana","first_name":"Adrian"},{"first_name":"Facundo","last_name":"Rodriguez","full_name":"Rodriguez, Facundo"},{"first_name":"David J","last_name":"Kedziora","full_name":"Kedziora, David J"},{"last_name":"Chadderdon","first_name":"George L","full_name":"Chadderdon, George L"},{"full_name":"Kerr, Cliff C","first_name":"Cliff C","last_name":"Kerr"},{"full_name":"Neymotin, Samuel A","last_name":"Neymotin","first_name":"Samuel A"},{"full_name":"McDougal, Robert A","last_name":"McDougal","first_name":"Robert A"},{"last_name":"Hines","first_name":"Michael","full_name":"Hines, Michael"},{"first_name":"Gordon MG","last_name":"Shepherd","full_name":"Shepherd, Gordon MG"},{"full_name":"Lytton, William W","last_name":"Lytton","first_name":"William W"}],"external_id":{"pmid":["31025934"],"isi":["000468968400001"]},"publication_identifier":{"issn":["2050-084X"]},"volume":8,"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."}],"_id":"7405","publication_status":"published","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","publisher":"eLife Sciences Publications","file":[{"checksum":"7014189c11c10a12feeeae37f054871d","date_created":"2020-02-04T08:41:47Z","file_name":"2019_eLife_DuraBernal.pdf","access_level":"open_access","relation":"main_file","file_id":"7444","creator":"dernst","date_updated":"2020-07-14T12:47:57Z","file_size":6182359,"content_type":"application/pdf"}],"date_created":"2020-01-30T09:08:01Z","status":"public","day":"31","isi":1,"department":[{"_id":"PeJo"}],"quality_controlled":"1","oa":1,"doi":"10.7554/elife.44494","article_type":"original","oa_version":"Published Version","file_date_updated":"2020-07-14T12:47:57Z","pmid":1,"type":"journal_article","citation":{"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.","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>.","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>","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.","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>.","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>","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_updated":"2023-09-07T14:27:52Z","has_accepted_license":"1","article_number":"e44494","ddc":["570"],"month":"05","date_published":"2019-05-31T00:00:00Z","scopus_import":"1","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"publication":"eLife","article_processing_charge":"No","language":[{"iso":"eng"}],"year":"2019","title":"NetPyNE, a tool for data-driven multiscale modeling of brain circuits","intvolume":"         8"}]