Summary
Memories are not stored in a stable manner in the brain. Instead they undergo an almost constant process of transformation and reorganization from the very moment of their formation. This phenomenon, going under the name of memory consolidation is also at the core of fundamental changes in the nature of the information carried by the neural representations. In fact, the switch from hippocampal-based short-term memories to cortex-based long-term memories also coincides with the transitions from episodic to semantic memories. In this proposal, we aim at investigating the role played in memory consolidation by stochasticity and criticality of neural dynamics. We will develop a quantitative approach to this question by combining statistical analysis of newly recorded data, with mathematical modelling of the cortical-hippocampal interaction. Our hypothesis takes the emergence of long-range correlation as the central mechanism characterizing semantic memory formation. We propose that cortical neural circuits poised at criticality can exploit activity avalanches associated with this state to induce the collective activation of large portions of the brain necessary to reconstruct the complete input statistics. Using a hierarchical neural network trained with an input of random samplings of the episodic representations stored in the hippocampus, we will explore the interaction between the performance of the network and its closeness to a critical state. We will complement this work with an analysis of the presence of criticality in the cortical cell assemblies formed after learning a complex behavioural task. We expect these cell assemblies to show maximal contribution to the propagation of neural avalanches and to present consistent coordination with the hippocampal activity. We intend to integrate these results in a coherent theory of long-term memory formation, explaining the ability to generalize starting from event-based experiences.
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| Web resources: | https://cordis.europa.eu/project/id/840704 |
| Start date: | 01-11-2019 |
| End date: | 31-10-2021 |
| Total budget - Public funding: | 175 572,48 Euro - 175 572,00 Euro |
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Original description
Memories are not stored in a stable manner in the brain. Instead they undergo an almost constant process of transformation and reorganization from the very moment of their formation. This phenomenon, going under the name of memory consolidation is also at the core of fundamental changes in the nature of the information carried by the neural representations. In fact, the switch from hippocampal-based short-term memories to cortex-based long-term memories also coincides with the transitions from episodic to semantic memories. In this proposal, we aim at investigating the role played in memory consolidation by stochasticity and criticality of neural dynamics. We will develop a quantitative approach to this question by combining statistical analysis of newly recorded data, with mathematical modelling of the cortical-hippocampal interaction. Our hypothesis takes the emergence of long-range correlation as the central mechanism characterizing semantic memory formation. We propose that cortical neural circuits poised at criticality can exploit activity avalanches associated with this state to induce the collective activation of large portions of the brain necessary to reconstruct the complete input statistics. Using a hierarchical neural network trained with an input of random samplings of the episodic representations stored in the hippocampus, we will explore the interaction between the performance of the network and its closeness to a critical state. We will complement this work with an analysis of the presence of criticality in the cortical cell assemblies formed after learning a complex behavioural task. We expect these cell assemblies to show maximal contribution to the propagation of neural avalanches and to present consistent coordination with the hippocampal activity. We intend to integrate these results in a coherent theory of long-term memory formation, explaining the ability to generalize starting from event-based experiences.Status
CLOSEDCall topic
MSCA-IF-2018Update Date
28-04-2024
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