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DopamineLearnLoops

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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... ver más

30/09/2028

TECHNION ISRAEL I...

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

TECHNION ISRAEL INSTITUTE OF TECHNOLOGY No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-09-29

ERC-2023-STG: ERC STARTING GRANTS Scope:Objectives

I+D

Cerrada hace 2 años

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Información adicional privada

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

1 Participantes

TECHNION ISRAEL INSTITUTE O...

1.50M€ | Lider

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Duración del proyecto: 60 meses Fecha Inicio: 2023-09-29
Fecha Fin: 2028-09-30

Líder del proyecto

TECHNION ISRAEL INSTITUTE OF TECHNOLOGY No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto 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.

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