SOURCE-LINKED INTELLIGENCE

Linking self-supervised learning to cortical circuits for uncertainty processing

CORDIS · observation · Publication date unknown

-driven approach. I will develop a theoretical framework that enables solving the problem in realistic scenarios, by combining rigorous and interpretable Bayesian modeling with modern self-supervised deep learning methods. This will provide insights into the determinants of dynamical weighting in diverse environments. In simulations I will test the hypothesis that the same weighting mechanisms are beneficial across environments, meaning that pre-structuring world models with these mechanisms consistently improves uncertainty processing. Finally, to understand if and how these mechanisms are implemented in cortex, I will collaborate with experimental researchers at FMI to record neural responses to complex stimuli with uncertainty in mice. By quantitatively predicting these responses we can directly measure the success of the developed theory, and assign functional roles to individual neurons in the brain. The results of this project will elucidate how specific cortical circuit elements enable to model an uncertain world - one of the most fundamental abilities of the brain. Cortical

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recordType

award

status

SIGNED

region

EU

value

292118.88

unit

EUR