Project description
Decoding brain computations
The brain is known to perform complex computations, yet our understanding of how distributed neural networks work together to achieve this is limited. The key objective of the ERC-funded NEURO-FUSE project is to delineate the contribution of individual neurons to broader, distributed cognitive processes, across several brain regions. The working hypothesis is that global brain dynamics fall into distinct attractor states during tasks, creating invariant representations independent of specific neurons. By inferring these invariances, the project will reconstruct brain-wide activity from local recordings in mouse and macaques. Machine learning will help reveal distributed cognitive processes, overcoming local sampling limitations in neuroscience.
Objective
Understanding how the coordinated activity of neurons in multiple brain regions achieves robust behaviour is one of the most fundamental questions in neuroscience. Although recent single-cell technologies enable addressing this question by recording from large neural populations, they are limited to surveying focal brain regions and superficial cortical layers. Without an analytical framework to jointly model isolated measurements, we cannot hope to understand and quantitatively model how single-neuron dynamics give rise to distributed computations. I hypothesise that global brain dynamics fall on distinct attractor states during a given stimulus or task. Attractors naturally give rise to invariant representations, dynamical motifs independent of the sampled neurons’ identity. Inferring these invariances would allow reconstructing activity in extended regions from incomplete local recordings to reveal brain-wide cognitive processes. Further, composing invariances would provide insights into the neural correlates of generalisation, with a broad impact on neuroscience and machine learning. I propose a novel mathematical theory combining abstract combinatorial dynamical systems theory and modern machine learning to infer and compose invariant latent dynamics across measurements. We will use this theory to unify large-scale cell-resolution recordings of the mouse and macaque cortex into a common model to make cell-specific predictions across several brain regions. Our results could fundamentally challenge our view on distributed cognitive computations by revealing moment-by-moment single-neuron dynamics in spatially distributed neurons. Further, my theory will help understand how the brain generalises knowledge across tasks by composing and repurposing invariances. More broadly, my theory will open new avenues for machine learning and neuroscience to interact through sharing and shaping the dynamical processes that underpin neural computations in vivo and in silico.
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.
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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)
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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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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.
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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.
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-STG
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1090 Wien
Austria
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