Project description
The brain’s learning mechanism for sensory predictions
Animals need to tell the difference between sensory information caused by their own movements and that from the outside world. This ability is essential for guiding behaviour, but it is a complex process. The brain learns to predict sensations generated by self-motion, using prediction errors to adjust its predictions. These errors occur when there is a mismatch between what the brain expects and what it actually senses. With this in mind, the ERC-funded Learn2Predict project explores how the brain learns from these prediction errors. By focusing on the visual cortex and the brain’s neuromodulatory system, the project aims to understand how the brain improves its sensory predictions. Using technologies like virtual reality and optogenetics, the researchers will explore how this learning process affects behaviour.
Objective
To perform sensory guided behaviors, animals need to distinguish self-generated and externally generated sensory inputs. Predictive processing theories propose that the brain does this by learning to predict sensations caused by self-motion. The key signals thought to drive this learning are prediction errors: differences between predicted and actual sensory input. My previous work shows that neurons in the primary visual cortex (V1) compute visuomotor prediction errors, and that prediction errors activate the locus coeruleus, a brain-wide neuromodulatory system. We will now investigate the circuit and neuromodulatory mechanisms underlying the learning of sensory predictions, using V1 as a model. I hypothesize that input to V1 from higher order cortical areas undergoes plasticity during self-generated sensory feedback. This plasticity should be driven by prediction errors in V1 activity, modulated by locus coeruleus output, and improve detection of externally generated visual flow during self-motion. We will test this hypothesis using innovative methods, including a multimodal virtual reality system and a novel object detection task, combined with in vivo whole cell recordings, two-photon imaging, and optogenetics. The specific aims are to (1) investigate how prediction errors are communicated between the locus coeruleus and the cortex, (2) decipher the mechanisms of predictive plasticity within the V1 circuit, and (3) assess the behavioral relevance of this plasticity. The knowledge gained will have a fundamental impact on our mechanistic understanding of predictive learning in the cortex and the role of neuromodulation in this process, which will have significance for 1) understanding conditions in which the processing of self-generated sensory feedback is thought to be disrupted (e.g. neurodevelopmental conditions and psychosis), and 2) development of AI and brain-machine interfaces that deal with self-generated sensor feedback (e.g. prostheses).
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- engineering and technology electrical engineering, electronic engineering, information engineering electronic engineering sensors
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
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(opens in new window) ERC-2024-STG
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EH8 9YL Edinburgh
United Kingdom
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