Summary
In this project I want to unravel how we build-up and update our knowledge networks depending on the amount of previous experience we have by testing my new theory on experience dependent learning. Current theory postulates memories to be stored initially as sets of neural representations spanning the hippocampus and weakly interacting neocortical modules. The spontaneous reactivation of new memories during a consolidation phase, would lead to strengthening of the neocortical memory trace. Consequently, pre-existing knowledge is proposed to be coded in the brain as a cortical network of neurons that allows for more efficient consolidation of new information. I recently developed a new theory, proposing that the existence of previous knowledge and therefore the extent of the cortical memory network, is a gradient of experience instead of being either present or not. The size and complexity of the knowledge network would influence where in the brain memories are encoded and how fast they are consolidated. I propose combining my innovative behavioural paradigm – the HexMaze - that enables investigatory access to various levels of experience, with different techniques in three species to test this theory. In mice, with immediate early gene expression techniques to visualize and manipulate the brain-wide memory network with the resolution of individual neurons. In rats, with electrophysiology to measure and manipulate memory reactivations during sleep as the mechanisms to enable consolidation. In humans, with targeted memory reactivation and magnetic resonance imaging to follow the evolution of learning over one year. The combination of species with their respective methods enables to observe effects as well as test for causality. The unique combination of meaningful behaviour with appropriate, precise techniques would provide ground-breaking insight into how we create and update our knowledge networks and change the way we view and test memory.
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| Web resources: | https://cordis.europa.eu/project/id/101122484 |
| Start date: | 01-06-2024 |
| End date: | 31-05-2029 |
| Total budget - Public funding: | 2 000 000,00 Euro - 2 000 000,00 Euro |
Cordis data
Original description
In this project I want to unravel how we build-up and update our knowledge networks depending on the amount of previous experience we have by testing my new theory on experience dependent learning. Current theory postulates memories to be stored initially as sets of neural representations spanning the hippocampus and weakly interacting neocortical modules. The spontaneous reactivation of new memories during a consolidation phase, would lead to strengthening of the neocortical memory trace. Consequently, pre-existing knowledge is proposed to be coded in the brain as a cortical network of neurons that allows for more efficient consolidation of new information. I recently developed a new theory, proposing that the existence of previous knowledge and therefore the extent of the cortical memory network, is a gradient of experience instead of being either present or not. The size and complexity of the knowledge network would influence where in the brain memories are encoded and how fast they are consolidated. I propose combining my innovative behavioural paradigm – the HexMaze - that enables investigatory access to various levels of experience, with different techniques in three species to test this theory. In mice, with immediate early gene expression techniques to visualize and manipulate the brain-wide memory network with the resolution of individual neurons. In rats, with electrophysiology to measure and manipulate memory reactivations during sleep as the mechanisms to enable consolidation. In humans, with targeted memory reactivation and magnetic resonance imaging to follow the evolution of learning over one year. The combination of species with their respective methods enables to observe effects as well as test for causality. The unique combination of meaningful behaviour with appropriate, precise techniques would provide ground-breaking insight into how we create and update our knowledge networks and change the way we view and test memory.Status
SIGNEDCall topic
ERC-2023-COGUpdate Date
12-03-2024
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