Summary
Spontaneous activity accounts for most of what the brain does and is likely to be key for information processing in the brain, but its function is still quite mysterious. Two key spontaneous activity processes are the Default Mode Network, a set of areas that are most markedly connected and active during behavioural idleness, and memory replay, the spontaneous reactivation of neural patterns occurring during experience.
I will test the hypothesis that the DMN plays a key role in memory replay processes. This theory, if confirmed, would bring important conceptual advances: to memory studies, as it would provide a mechanism supporting the formation and consolidation of complex memory representations. To the Default Mode Network field, as replay can be used as the “Rosetta Stone” to decipher the computations the DMN performs, moving beyond the connectivity, dynamics, and cognitive correlates, typical focus of DMN research.
I will explore this theory by an experimental study of spontaneous neural activity over the whole mouse cortex, going from large field-of-view 2-photon imaging and high-volume electrophysiology for the single neuron scale, to voltage sensitive imaging and electrocorticography, to resting state fMRI, in animals running memory tasks.
I will characterize the network dynamics and the encoding and replay of memories by quantifying conveyed information and assessing its nature (e.g. about simple percepts vs. complex events, remote vs. memories). I will also measure critical behaviour in these networks, and test whether neuronal avalanches, that occur in spontaneous activity, play a role in conveying information across distant brain areas.
I will model the consequences of these mechanisms for computation by formulating a machine learning based model of memory formation and consolidation, endowing a deep network with critical properties and memory replay.
I will test the hypothesis that the DMN plays a key role in memory replay processes. This theory, if confirmed, would bring important conceptual advances: to memory studies, as it would provide a mechanism supporting the formation and consolidation of complex memory representations. To the Default Mode Network field, as replay can be used as the “Rosetta Stone” to decipher the computations the DMN performs, moving beyond the connectivity, dynamics, and cognitive correlates, typical focus of DMN research.
I will explore this theory by an experimental study of spontaneous neural activity over the whole mouse cortex, going from large field-of-view 2-photon imaging and high-volume electrophysiology for the single neuron scale, to voltage sensitive imaging and electrocorticography, to resting state fMRI, in animals running memory tasks.
I will characterize the network dynamics and the encoding and replay of memories by quantifying conveyed information and assessing its nature (e.g. about simple percepts vs. complex events, remote vs. memories). I will also measure critical behaviour in these networks, and test whether neuronal avalanches, that occur in spontaneous activity, play a role in conveying information across distant brain areas.
I will model the consequences of these mechanisms for computation by formulating a machine learning based model of memory formation and consolidation, endowing a deep network with critical properties and memory replay.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/833964 |
| Start date: | 01-09-2019 |
| End date: | 31-08-2024 |
| Total budget - Public funding: | 2 375 000,00 Euro - 2 375 000,00 Euro |
Cordis data
Original description
Spontaneous activity accounts for most of what the brain does and is likely to be key for information processing in the brain, but its function is still quite mysterious. Two key spontaneous activity processes are the Default Mode Network, a set of areas that are most markedly connected and active during behavioural idleness, and memory replay, the spontaneous reactivation of neural patterns occurring during experience.I will test the hypothesis that the DMN plays a key role in memory replay processes. This theory, if confirmed, would bring important conceptual advances: to memory studies, as it would provide a mechanism supporting the formation and consolidation of complex memory representations. To the Default Mode Network field, as replay can be used as the “Rosetta Stone” to decipher the computations the DMN performs, moving beyond the connectivity, dynamics, and cognitive correlates, typical focus of DMN research.
I will explore this theory by an experimental study of spontaneous neural activity over the whole mouse cortex, going from large field-of-view 2-photon imaging and high-volume electrophysiology for the single neuron scale, to voltage sensitive imaging and electrocorticography, to resting state fMRI, in animals running memory tasks.
I will characterize the network dynamics and the encoding and replay of memories by quantifying conveyed information and assessing its nature (e.g. about simple percepts vs. complex events, remote vs. memories). I will also measure critical behaviour in these networks, and test whether neuronal avalanches, that occur in spontaneous activity, play a role in conveying information across distant brain areas.
I will model the consequences of these mechanisms for computation by formulating a machine learning based model of memory formation and consolidation, endowing a deep network with critical properties and memory replay.
Status
SIGNEDCall topic
ERC-2018-ADGUpdate Date
27-04-2024
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