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BrainPlay

Financiado

The self teaching brain

Our goal is to uncover the learning algorithms that subserve biological intelligence and to discover how they are implemented in the brain. We take for granted that biological intelligence results from neural information processin... ver más

31/01/2026

UBER

10M€

Presupuesto del proyecto: 10M€

Líder del proyecto

HUMBOLDTUNIVERSITAET ZU BERLIN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 4 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2019-05-17

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

ERC-2018-SyG: ERC Synergy Grant

I+D

Cerrada hace 7 años

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4 Participantes

UBER

2.50M€ | Lider

DULCESA

479.94K€ | Participante

CHARITE UNIVERSITAETSMEDIZI...

4.78M€ | Participante

Últimas noticias

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

29-11-2024: CMVAMADRID En las últimas 48 horas el Organismo CMVAMADRID ha otorgado 37 concesiones

Duración del proyecto: 80 meses Fecha Inicio: 2019-05-17
Fecha Fin: 2026-01-31

Líder del proyecto

HUMBOLDTUNIVERSITAET ZU BERLIN

TRL 4-5

Presupuesto del proyecto 10M€

Descripción del proyecto Our goal is to uncover the learning algorithms that subserve biological intelligence and to discover how they are implemented in the brain. We take for granted that biological intelligence results from neural information processing, that neural information processing is based on the transmission of action potentials through synapses, and that learning is realized through synaptic plasticity. We are inspired by two key observations: Firstly, we know that biological learning unfolds in ways different from mainstream machine learning that relies on learning from large labeled datasets. Second, we discovered that the engagement of the brain during play can result in unexpected and profound cognitive benefits. This proposal describes an untravelled route to the learning algorithms of the brain that runs through the no-man’s-land between synaptic physiology, systems neuroscience, cognitive neuroscience, theoretical neuroscience and machine learning. Our approach focuses on the self-teaching abilities of the mammalian brain and covers and connects four major topics: (1) the objective functions that govern synaptic plasticity, (2) the teaching signals through which learning is steered, (3) behavioral mechanisms of self-teaching, in particular play behaviors, (4) the brain states that engage self-teaching behaviors, in particular the brain state of play. The BrainPlay grant will study self-teaching abilities from synapses to brains, from computational theory to action video games. As gaming has been shown to be highly beneficial for human brain function, we are intrigued by how little we know about what is going on in playing brains and how the brain state of play shapes learning. Engaging the latest theoretical and technological breakthroughs, BrainPlay will reach far beyond mainstream neuroscience and embrace and elucidate playfulness and self-teaching as important components of the brain's learning algorithms.

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