A theory and model of the neural transformations mediating human object perception
TRANSFORM aims to develop a predictive model and theory of neural transformations for object perception by integrating brain imaging, mathematical analysis, and computational modeling.
Projectdetails
Introduction
In the blink of an eye, our brain rapidly transforms the photons hitting our retina into a rich and detailed percept of the world as consisting of objects. By knowing what the objects are, and in what configuration these objects appear to us, we understand the meaning of the visual world around us. Yet, despite intense research, how neural transformations enable rich object perception remains unclear.
Research Goals
The overall goal of the research program TRANSFORM is to provide an explanatory theory of the neural transformations mediating rich object perception, and a predictive quantitative model embodying this theory.
Constraints for Theory and Model Building
Towards this goal, TRANSFORM will provide three strong and novel constraints for theory and model building:
- Neural Constraint: TRANSFORM will reveal the neural transformations underlying visual object perception in the mature brain.
- Behavioural Constraint: It will unravel the link between the neural transformation and object-related behaviour.
- Developmental Constraint: It will clarify the developmental trajectory of neural transformations underlying visual object perception from infancy into adulthood.
Research Strategy
For maximal efficiency and power in unified theory formation and model building, TRANSFORM will employ an integrated, interdisciplinary research strategy that combines:
- Large-scale non-invasive brain imaging to capture neural transformations in space and time with unprecedented depth.
- Advanced mathematical analysis to reveal the geometry of the transformations.
- Computational modelling using deep neural networks to build a predictive, quantitative model of those transformations.
Conclusion
Through this orchestrated effort, TRANSFORM will provide the empirical pieces of evidence for a new theory and model of the neural transformations mediating our rich everyday experience of object vision and change the way we think about and investigate human vision and cognition.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 2.291.855 |
Totale projectbegroting | € 2.291.855 |
Tijdlijn
Startdatum | 1-4-2025 |
Einddatum | 31-3-2030 |
Subsidiejaar | 2025 |
Partners & Locaties
Projectpartners
- FREIE UNIVERSITAET BERLINpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
Project | Regeling | Bedrag | Jaar | Actie |
---|---|---|---|---|
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 |
Reading the mind’s eye: AI-inspired personalised brain models of mental imageryThis project aims to develop a personalized AI model of mental imagery by decoding neural activity and predicting image vividness, enhancing understanding and training of mental visualization. | ERC Advanced... | € 2.498.288 | 2024 | Details |
How EXPectation and ATtention shape visual information processing in the human brainEXPAT aims to uncover how expectation and attention influence visual information encoding in the human brain using advanced neuroimaging and analytical techniques. | ERC Consolid... | € 1.999.815 | 2023 | Details |
Uncovering the core dimensions of visual object representationsCOREDIM aims to identify the core dimensions of visual object representations using neuroimaging, behavioral data, and AI, enhancing our understanding of visual processing in the brain. | ERC Starting... | € 1.500.000 | 2022 | Details |
Using deep neural networks to understand functional specialization in the human visual cortexThis project aims to uncover the origins of functional specialization in the brain's visual pathway by integrating computational modeling, naturalistic behavior sampling, and neuroimaging. | ERC Starting... | € 1.494.750 | 2024 | Details |
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.
Reading the mind’s eye: AI-inspired personalised brain models of mental imagery
This project aims to develop a personalized AI model of mental imagery by decoding neural activity and predicting image vividness, enhancing understanding and training of mental visualization.
How EXPectation and ATtention shape visual information processing in the human brain
EXPAT aims to uncover how expectation and attention influence visual information encoding in the human brain using advanced neuroimaging and analytical techniques.
Uncovering the core dimensions of visual object representations
COREDIM aims to identify the core dimensions of visual object representations using neuroimaging, behavioral data, and AI, enhancing our understanding of visual processing in the brain.
Using deep neural networks to understand functional specialization in the human visual cortex
This project aims to uncover the origins of functional specialization in the brain's visual pathway by integrating computational modeling, naturalistic behavior sampling, and neuroimaging.