Learning offline: memory replay in biological and artificial reinforcement learning.
Trends Neurosci
; 44(10): 808-821, 2021 10.
Article
en En
| MEDLINE
| ID: mdl-34481635
ABSTRACT
Learning to act in an environment to maximise rewards is among the brain's key functions. This process has often been conceptualised within the framework of reinforcement learning, which has also gained prominence in machine learning and artificial intelligence (AI) as a way to optimise decision making. A common aspect of both biological and machine reinforcement learning is the reactivation of previously experienced episodes, referred to as replay. Replay is important for memory consolidation in biological neural networks and is key to stabilising learning in deep neural networks. Here, we review recent developments concerning the functional roles of replay in the fields of neuroscience and AI. Complementary progress suggests how replay might support learning processes, including generalisation and continual learning, affording opportunities to transfer knowledge across the two fields to advance the understanding of biological and artificial learning and memory.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
/
Hipocampo
Límite:
Humans
Idioma:
En
Revista:
Trends Neurosci
Año:
2021
Tipo del documento:
Article