Learning how network structure shapes decision-making for bio-inspired computing

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• dc.contributor.author Schirner, Michael
• dc.contributor.author Deco, Gustavo
• dc.contributor.author Ritter, Petra
• dc.date.accessioned 2023-07-07T07:01:18Z
• dc.date.available 2023-07-07T07:01:18Z
• dc.date.issued 2023
• dc.description.abstract To better understand how network structure shapes intelligent behavior, we developed a learning algorithm that we used to build personalized brain network models for 650 Human Connectome Project participants. We found that participants with higher intelligence scores took more time to solve difficult problems, and that slower solvers had higher average functional connectivity. With simulations we identified a mechanistic link between functional connectivity, intelligence, processing speed and brain synchrony for trading accuracy with speed in dependence of excitation-inhibition balance. Reduced synchrony led decision-making circuits to quickly jump to conclusions, while higher synchrony allowed for better integration of evidence and more robust working memory. Strict tests were applied to ensure reproducibility and generality of the obtained results. Here, we identify links between brain structure and function that enable to learn connectome topology from noninvasive recordings and map it to inter-individual differences in behavior, suggesting broad utility for research and clinical applications.
• dc.description.sponsorship Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. This work was supported by the Virtual Research Environment at the Charité Berlin – a node of EBRAINS Health Data Cloud. We gratefully acknowledge support by Digital Europe Grant TEF-Health # 101100700 (P.R.); H2020 Research and Innovation Action Grant Human Brain Project SGA2 785907 (P.R.); H2020 Research and Innovation Action Grant Human Brain Project SGA3 945539 (P.R.); H2020 Research and Innovation Action Grant Interactive Computing E-Infrastructure for the Human Brain Project ICEI 800858 (P.R.); H2020 Research and Innovation Action Grant EOSC VirtualBrainCloud 826421 (P.R.); H2020 Research and Innovation Action Grant AISN 101057655 (P.R.); H2020 Research Infrastructures Grant EBRAINS-PREP 101079717 (P.R.); H2020 European Innovation Council PHRASE 101058240 (P.R.); H2020 Research Infrastructures Grant EBRAIN-Health 101058516 (P.R.); H2020 European Research Council Grant ERC BrainModes 683049 (P.R.); JPND ERA PerMed PatternCog 2522FSB904 (P.R.); Berlin Institute of Health & Foundation Charité (P.R.); Johanna Quandt Excellence Initiative (P.R.); German Research Foundation SFB 1436 (project ID 425899996) (P.R.); German Research Foundation SFB 1315 (project ID 327654276) (P.R.); German Research Foundation SFB 936 (project ID 178316478) (P.R.); German Research Foundation SFB-TRR 295 (project ID 424778381) (P.R.); German Research Foundation SPP Computational Connectomics RI 2073/6-1, RI 2073/10-2, RI 2073/9-1 (P.R.).
• dc.format.mimetype application/pdf
• dc.identifier.citation Schirner M, Deco G, Ritter P. Learning how network structure shapes decision-making for bio-inspired computing. Nat Commun. 2023;14:2963. DOI: 10.1038/s41467-023-38626-y
• dc.identifier.doi http://dx.doi.org/10.1038/s41467-023-38626-y
• dc.identifier.issn 2041-1723
• dc.identifier.uri http://hdl.handle.net/10230/57502
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Nature Communications. 2023;14:2963.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/785907
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/945539
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/800858
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/826421
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/683049
• dc.relation.projectID info:eu-repo/grantAgreement/EC/HE/101057655
• dc.relation.projectID info:eu-repo/grantAgreement/EC/HE/101058240
• dc.relation.projectID info:eu-repo/grantAgreement/EC/HE/101058516
• dc.relation.projectID info:eu-repo/grantAgreement/EC/HE/101079717
• dc.rights © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Biophysical models
• dc.subject.keyword Computational models
• dc.subject.keyword Dynamical systems
• dc.subject.keyword Intelligence
• dc.subject.keyword Network models
• dc.title Learning how network structure shapes decision-making for bio-inspired computing
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion