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学術論文

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

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Deco,  Gustavo
Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain;
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Catalan Institution for Research and Advanced Studies (ICREA), University Pompeu Fabra, Barcelona, Spain;
School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia;

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フルテキスト (公開)

Schirner_2023.pdf
(出版社版), 2MB

付随資料 (公開)

Schirner_2023_Suppl.pdf
(付録資料), 3MB

Schirner_2023_Suppl1.pdf
(付録資料), 76KB

Schirner_2023_Suppl2.mp4
(付録資料), 16MB

引用

Schirner, M., Deco, G., & Ritter, P. (2023). Learning how network structure shapes decision-making for bio-inspired computing. Nature Communications, 14(1):. doi:10.1038/s41467-023-38626-y.


引用: https://hdl.handle.net/21.11116/0000-000D-3454-A
要旨
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.