Project description
Understanding the role of cortical circuits in brain model uncertainty
The brain models the world by prioritising reliable sensory information and predictions, especially under uncertainty. However, existing models are too simplistic to effectively connect these theories with neural mechanisms. Supported by the Marie Skłodowska-Curie Actions programme, the CIRCUIT-LINK project investigates how the brain addresses the dynamic weighting problem using a theoretical and data-driven approach. It will develop a framework combining Bayesian modelling and self-supervised deep learning to identify factors influencing dynamic weighting in various environments. The project will also measure neural responses in mice to stimuli, involving uncertainty. The findings will enhance our understanding of how cortical circuits help the brain model uncertainty, a crucial cognitive ability.
Objective
The brain has the remarkable ability to maintain a robust model of the world even when sensory data is ambiguous and noisy. To achieve this, it dynamically prioritizes sensory observations or internal predictions depending on their reliability. When observations are unreliable, e.g. in low visibility conditions, they are weighted less compared to predictions, and vice versa. While previous studies have proposed intriguing hypotheses on how neural circuits could solve this dynamic weighting problem, a clear and empirically testable connection between theory and neural mechanisms is still missing. The main reason is that current theories are highly simplified, and are not able to quantitatively predict responses of a sensory system that is adapted to a highly complex environment.
This project will investigate how the brain solves the dynamic weighting problem via a theory- and data-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.
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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(opens in new window) HORIZON-MSCA-2025-PF
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4056 Basel
Switzerland
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