{"publication_identifier":{"issn":["2662-8457"]},"type":"journal_article","month":"12","date_published":"2021-12-16T00:00:00Z","file_date_updated":"2022-06-18T22:30:03Z","keyword":["general medicine"],"date_created":"2022-03-04T08:32:36Z","oa_version":"Submitted Version","article_type":"original","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","has_accepted_license":"1","status":"public","ec_funded":1,"acknowledged_ssus":[{"_id":"SSU"}],"title":"How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex–dentate gyrus–CA3 network","ddc":["610"],"intvolume":"         1","citation":{"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>.","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>","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>.","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.","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.","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>","short":"J. Guzmán, A. Schlögl, C. Espinoza Martinez, X. Zhang, B. Suter, P.M. Jonas, Nature Computational Science 1 (2021) 830–842."},"author":[{"first_name":"José","id":"30CC5506-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2209-5242","full_name":"Guzmán, José","last_name":"Guzmán"},{"full_name":"Schlögl, Alois","last_name":"Schlögl","first_name":"Alois","orcid":"0000-0002-5621-8100","id":"45BF87EE-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Claudia ","orcid":"0000-0003-4710-2082","id":"31FFEE2E-F248-11E8-B48F-1D18A9856A87","full_name":"Espinoza Martinez, Claudia ","last_name":"Espinoza Martinez"},{"last_name":"Zhang","full_name":"Zhang, Xiaomin","id":"423EC9C2-F248-11E8-B48F-1D18A9856A87","first_name":"Xiaomin"},{"full_name":"Suter, Benjamin","last_name":"Suter","first_name":"Benjamin","id":"4952F31E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-9885-6936"},{"full_name":"Jonas, Peter M","last_name":"Jonas","first_name":"Peter M","id":"353C1B58-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-5001-4804"}],"department":[{"_id":"PeJo"}],"related_material":{"link":[{"relation":"press_release","url":"https://ista.ac.at/en/news/spot-the-difference/"}],"record":[{"id":"10110","status":"public","relation":"software"}]},"quality_controlled":"1","scopus_import":"1","project":[{"grant_number":"692692","_id":"25B7EB9E-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"Biophysics and circuit function of a giant cortical glumatergic synapse"},{"call_identifier":"FWF","name":"The Wittgenstein Prize","grant_number":"Z00312","_id":"25C5A090-B435-11E9-9278-68D0E5697425"}],"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."}],"day":"16","volume":1,"year":"2021","publisher":"Springer Nature","language":[{"iso":"eng"}],"oa":1,"publication_status":"published","doi":"10.1038/s43588-021-00157-1","issue":"12","page":"830-842","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.).","file":[{"file_id":"11430","relation":"main_file","creator":"patrickd","file_name":"Guzmanetal2021.pdf","access_level":"open_access","checksum":"9fec5b667909ef52be96d502e4f8c2ae","date_updated":"2022-06-18T22:30:03Z","content_type":"application/pdf","date_created":"2022-06-02T12:51:07Z","file_size":1699466,"embargo":"2022-06-17"},{"embargo":"2022-06-17","content_type":"application/pdf","file_size":3005651,"date_created":"2022-06-02T12:53:47Z","date_updated":"2022-06-18T22:30:03Z","checksum":"52a005b13a114e3c3a28fa6bbe8b1a8d","creator":"patrickd","access_level":"open_access","title":"Supplementary Material","file_name":"Guzmanetal2021Suppl.pdf","file_id":"11431","relation":"supplementary_material"}],"date_updated":"2023-08-10T22:30:10Z","main_file_link":[{"url":"https://www.biorxiv.org/content/10.1101/647800","open_access":"1"}],"publication":"Nature Computational Science","_id":"10816"}