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

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

SUBMITTER: Schirner M 

PROVIDER: S-EPMC10206104 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature