DeepStore
Financiado
The Dynamic Representational Nature of Working Memory Storage
Working memory excels in keeping ready the momentary contents of all of human higher cognition despite being strictly limited in storage capacity. How the brain accomplishes this feat, beyond the temporary maintenance of just expe...
Working memory excels in keeping ready the momentary contents of all of human higher cognition despite being strictly limited in storage capacity. How the brain accomplishes this feat, beyond the temporary maintenance of just experienced information, is hardly understood. An emerging view suggests that working memory storage is topographically distributed according to the information’s endogenous level of abstraction. However, very little is known about how such distributed storage is orchestrated spatiotemporally, that is, how representations at different levels of abstraction are structured dynamically in time to suit current demands.
In DeepStore, we use tailored experimental designs and newly developed multivariate analysis techniques to explore for the first time directly the depth-dimension of working memory storage, in terms of dynamic levels of abstraction. Combining functional imaging, magneto-/electroencephalography, and invasive neural recordings will make it possible to track levels of abstraction spatiotemporally, with millisecond precision.
In three ambitious experimental series, DeepStore will shed new light on long-standing open questions about working memory storage, including how it is modulated by attention, inattention, and distraction, and how multiple contents are stored simultaneously. The work will shape a new theory of the neurocognitive capacity limit in working memory. We will further test novel hypotheses about how working memory interfaces with long-term memory, and how it develops over the lifespan. Finally, we will combine our innovative human neuroimaging approach with direct electrophysiological recordings in non-human primates during the same task, to disclose the fine-grained neural mechanisms of dynamic abstraction down to the single-cell level. DeepStore is anticipated to provide fundamentally new insights into the dynamic and multi-layered nature of working memory, beyond the number and precision of items it can hold.
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Duración del proyecto: 68 meses
Fecha Inicio: 2021-04-15
Fecha Fin: 2026-12-31
Fecha Fin: 2026-12-31
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2021-04-15
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2020-COG:
ERC CONSOLIDATOR GRANTS
Cerrada
hace 4 años
Presupuesto
El presupuesto total del proyecto asciende a
2M€
Líder del proyecto
MAXPLANCKGESELLSCHAFT ZUR FORDERUNG DER WISSE...
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
118.80K€ |
Participante