Active Inference and the Circuits of Precision and Prediction
PREDICTION aims to uncover the neural mechanisms of high-level visual cognition by integrating advanced methods across disciplines to model hierarchical processing in the human brain.
Projectdetails
Introduction
The human brain exhibits remarkable cognitive abilities, such as imagery, attention, and mental simulation, which along with perception reflect our capacity to infer properties of the world. Perceptual inference involves a statistical hierarchical synthesis of information from the senses with prior knowledge of the world.
Predictive Processing
There is empirical evidence in favor of Predictive Processing, where functional interaction of descending pathways carrying top-down prediction and precision signals interacts with the ascending pathways to generate prediction errors.
Project Goals
PREDICTION will develop a mechanistic and computational understanding of the hierarchical processing underlying inference in vision-related human brain areas. It will establish how feedback connections enable generative cognitive functions.
Methodology
Unraveling the neural mechanisms of high-level visual cognitive functions requires novel ways of thinking about biological information processing and an integrated effort to measure and model cognitive functions at multiple brain scales. PREDICTION accomplishes this by integrating concepts and advanced methods from multiple disciplines:
- Molecular biology to characterize projection cell-types.
- Psychophysics and sub-millimeter ultra-high-field functional imaging of cortical layers and columns in the human brain.
- Precision viral tracing to investigate the anatomical origins and targets of generative networks in non-human primates (NHP).
- Spatio-temporal controlled activation of genetically identified feedback pathways with optogenetics together in multi-areal laminar recordings in NHPs.
- Biologically realistic multi-scale computational modeling.
Expected Outcomes
PREDICTION will describe the functional circuits supporting hierarchical processing and their role in active vision. This will take us to the heart of cortical function and its causative role in perception, cognition, and decision-making, relative to a range of pathologies, as well as to understand what makes us human.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 2.500.000 |
Totale projectbegroting | € 2.500.000 |
Tijdlijn
Startdatum | 1-1-2025 |
Einddatum | 31-12-2029 |
Subsidiejaar | 2025 |
Partners & Locaties
Projectpartners
- INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALEpenvoerder
- UNIVERSITEIT MAASTRICHT
Land(en)
Vergelijkbare projecten binnen European Research Council
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It's about time: Towards a dynamic account of natural vision.TIME aims to revolutionize vision research by integrating semantic understanding and active information sampling through advanced brain imaging and bio-inspired deep learning, enhancing insights into visual cognition. | ERC Starting... | € 1.499.455 | 2022 | Details |
Circuit mechanisms of cortical predictive learning
This project aims to investigate the circuit and neuromodulatory mechanisms of sensory prediction learning in the visual cortex, enhancing understanding of self-generated feedback processing and its implications for neurodevelopmental conditions and AI.
Empirical and mechanistic foundations for synergistic predictive processing in the sensory brain
SynPrePro aims to integrate hierarchical predictive coding with subcortical processing to enhance understanding of sensory input processing and its implications for perceptual disorders.
Making sense of the senses: Causal Inference in a complex dynamic multisensory world
This project aims to uncover how the brain approximates causal inference in complex multisensory environments using interdisciplinary methods, potentially informing AI and addressing perceptual challenges in clinical populations.
Tracing Visual Computations from the Retina to Behavior
This project aims to investigate how the superior colliculus integrates retinal signals to drive behavior using imaging, optogenetics, and modeling, revealing mechanisms of visual information processing.
It's about time: Towards a dynamic account of natural vision.
TIME aims to revolutionize vision research by integrating semantic understanding and active information sampling through advanced brain imaging and bio-inspired deep learning, enhancing insights into visual cognition.
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