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DYNAMIC_ENGRAM

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Deciphering the enigma of memory persistence how the brain stably stores inform...

Deciphering the enigma of memory persistence how the brain stably stores information using dynamic networks and unstable neurons How does the brain store and retrieve information over time? The accepted notion that these processes rely on the neuronal ensembles that were active during learning is now challenged by findings by our lab and others that reveal... ver más

31/01/2026

WEIZMANN

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

WEIZMANN INSTITUTE OF SCIENCE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-01-27

ERC-2020-COG: ERC CONSOLIDATOR GRANTS

I+D

Cerrada hace 5 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

WEIZMANN

2.00M€ | Lider

INDAG

327.42K€ | Participante

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Duración del proyecto: 60 meses Fecha Inicio: 2021-01-27
Fecha Fin: 2026-01-31

Líder del proyecto

WEIZMANN INSTITUTE OF SCIENCE No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto How does the brain store and retrieve information over time? The accepted notion that these processes rely on the neuronal ensembles that were active during learning is now challenged by findings by our lab and others that reveal that different neurons and networks than those that were active during learning support persistent memory. Most notably, we found that the long-term persistence of spatial memory is correlated with the degree to which neuronal activity is spatially informative, but not with the stability of the coding carried by individual neurons. These discoveries—obtained via novel imaging technologies that enable, for the first time, to track large populations of the same neurons over weeks—expose a fundamental gap in our understanding and highlight the need to reveal how neural codes across brain circuits, including the hippocampus, entorhinal cortex, and prefrontal cortex, change over the lifetime of a memory. Here we propose to investigate the mechanisms that govern the reorganization of memory using innovative methods we recently developed for optical imaging, large-scale data analysis, and circuit manipulation. Key among them is our ability to simultaneously and longitudinally image in two related brain areas the activity of large neuronal populations in freely behaving mice. Using these new tools, we will elucidate the factors governing the circuit dynamics of memory representations (Aim 1); how such dynamics relate to the behavioral manifestation of memory (Aim 2); how hippocampal-cortical and cortical-cortical interactions change over weeks to support remote memory (Aim 3); and what mechanisms could underlie the transfer of learned information between neurons in a network (Aim 4). Our approach will allow us to resolve how systems-level consolidation is realized at the neural code level, both within and across brain areas, and how a stable memory is maintained over the long term despite an ever-changing neuronal representation.

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