Objective
Deep generative networks offer a step change in understanding the neural mechanisms supporting human cognition. These networks, trained to predict their own inputs, learn abstract conceptual representations, and schema for translating them into imagined experience, forming a predictive model of the world.
GENERATIVE-MP will build from the “complementary learning systems” idea, of hippocampus storing episodic experience and replaying it to consolidate semantic knowledge in neocortex, by treating neocortex as a deep generative network. In this view, memories can be efficiently stored in hippocampus as a conceptual outline together with episode-unique details, because the concepts are learned to enable neocortical (re)construction of episodes. We will test three fundamental predictions via psychological and functional neuroimaging experiments while people experience virtual reality, stories and artificial stimuli:
1). Memories will become more schematic, with fewer veridical details, as retrieval moves from hippocampus to neocortex with consolidation;
2) Efficient hippocampal episodic representations should group together conceptually similar memories, and change less to encode episodes containing predictable details;
3) Consolidation of sequential experience captures predictive relationships between the elements of experience to enable planning of movements between them. If these relationships have a 2D (map-like) structure, grid cell-like representations in neocortex will support such planning.
GENERATIVE-MP will use the results to develop a hippocampal-generative neocortical model capable of simulating and interpreting them.
The outcome will be a comprehensive understanding of episodic memory and consolidation of semantic and conceptual knowledge for planning, implemented in a neural network that processes realistic stimuli. This will provide a platform for understanding how specific sets of stimuli are processed, e.g. to facilitate learning and avoid pitfalls.
Fields of science (EuroSciVoc)
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.
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.
- natural sciences biological sciences neurobiology cognitive neuroscience
- natural sciences computer and information sciences software software applications virtual reality
- natural sciences computer and information sciences artificial intelligence computational intelligence
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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)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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)
MAIN PROGRAMME
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Topic(s)
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.
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.
Funding Scheme
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.
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.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2025-ADG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
WC1E 6BT London
United Kingdom
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.