DopamineLearnLoops
Financiado
Toward a new understanding of learning in the brain: dynamic parallel circuit lo...
Toward a new understanding of learning in the brain: dynamic parallel circuit loops for complex learning
The brain’s ability to learn is arguably its most exceptional capacity. Learning in biological brains far surpasses machine learning and requires much less training. How does the brain accomplish this? Why is biological learning s...
The brain’s ability to learn is arguably its most exceptional capacity. Learning in biological brains far surpasses machine learning and requires much less training. How does the brain accomplish this? Why is biological learning still better than the most advanced machine learning algorithms to date? According to the standard model of reward-based learning in the brain, a single error signal is broadcast from the dopamine system and used to update the entire network, implementing a simple form of reinforcement learning. However, the standard model fails to predict several recent experimental findings, leaving open the question of how learning is implemented in the brain. In this project, I propose a new theory of how the brain learns: learning is implemented by multiple dopamine-based learning systems working in parallel circuit loops. These loops relay partial error signals to specific processing areas and permit independent evaluation of the value of different features in the external environment as well as the internal state, enabling learning of complex tasks with multiple relevant features. The loops are engaged dynamically according to the demands of the task, enabling the system to be flexible for learning a wide variety of behaviours of varying complexity. The presence of multiple dynamic parallel learning loops might enable the ability to generalize learning, which is currently the hallmark of biological intelligence. We will use state-of-the art techniques under the framework of our theory to elucidate basic mechanisms underlying the functional circuitry of the learning system (Aim 1), how it operates under different behavioural dynamics (Aim 2), and what algorithm it implements (Aim 3). Success of this project will enable a novel understanding of how the brain learns complex tasks as well as pave the way for the development of new brain-inspired deep reinforcement-learning algorithms.
ver más
Duración del proyecto: 60 meses
Fecha Inicio: 2023-09-29
Fecha Fin: 2028-09-30
Fecha Fin: 2028-09-30
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-09-29
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2023-STG:
ERC STARTING GRANTS
Cerrada
hace 2 años
Presupuesto
El presupuesto total del proyecto asciende a
1M€
Líder del proyecto
TECHNION ISRAEL INSTITUTE OF TECHNOLOGY
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5