The neural mechanism of scale-invariant creative search
CreativeBrain aims to unify behavioral, computational, and neurobiological insights into a mechanistic theory of creative search in the brain using scale-invariant sensing and Pareto optimality.
Projectdetails
Introduction
The human brain efficiently searches enormous mental spaces of thought for solutions to a given question. How is this done? The growing importance of creative search has sparked a surge of interest in mapping the relations between people’s creative search strategies and the structure, activity, and connectivity of their brain.
Current Challenges
Yet to date, there are no coherent computational principles to bind behavioral, computational, and neurobiological findings together into a mechanistic understanding of creative search.
Computational Principles
CreativeBrain uses two such computational principles:
- Scale-invariant sensing: This is essential for a robust search in environments with signals that span many orders of magnitude, like in creative search.
- Pareto optimality: This asserts that individual differences stem from different balancing between competing tasks that need to be optimized, thus explaining the utility of these individual differences.
Methodology
CreativeBrain will employ state-of-the-art computational and analysis methods from systems biology to infer the neural mechanism of creative search on the levels of function, computations, and neural implementation.
Research Impact
This will be the first comprehensive research effort that ties these findings to a mechanistic theory of creative search that also explains the utility of neural individual differences. The project will result in a breakthrough in our understanding of how the human brain can efficiently search in vast spaces of thought.
Broader Implications
On a broader scale, CreativeBrain will open a new front in the interdisciplinary studies of the mind and brain, offering a principled way to unite neurobiological, behavioral, and computational aspects into one holistic and mechanistic view.
By doing so, it will contribute significantly to the promise of computational modeling for connecting different levels of inquiry of a higher cognitive function in the brain and can thus be extended to other cognitive processes and systems.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.998.000 |
Totale projectbegroting | € 1.998.000 |
Tijdlijn
Startdatum | 1-5-2023 |
Einddatum | 30-4-2028 |
Subsidiejaar | 2023 |
Partners & Locaties
Projectpartners
- THE HEBREW UNIVERSITY OF JERUSALEMpenvoerder
Land(en)
Vergelijkbare projecten binnen European Research Council
Project | Regeling | Bedrag | Jaar | Actie |
---|---|---|---|---|
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Understanding diversity in decision strategy: from neural circuits to behavior
This project aims to uncover the neural mechanisms behind the brain's flexibility in decision-making strategies during foraging, using advanced computational and electrophysiological methods in mice.
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.
Mesoscale dissection of neuronal populations underlying cognition
This project aims to map cognitive processing in the brain using a mouse model, employing a zoom-out/zoom-in approach to understand dynamic networks across various cognitive functions.
Beyond mapping of the human brain: causal deconstruction of brain mechanisms underlying complex social behaviors
This project aims to explore the neural mechanisms of social information processing through innovative behavioral tasks and neurofeedback, enhancing understanding and treatment of social 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.