The computational foundations of dynamic coding in working memory.
Trends Cogn Sci
; 28(7): 614-627, 2024 Jul.
Article
em En
| MEDLINE
| ID: mdl-38580528
ABSTRACT
Working memory (WM) is a fundamental aspect of cognition. WM maintenance is classically thought to rely on stable patterns of neural activities. However, recent evidence shows that neural population activities during WM maintenance undergo dynamic variations before settling into a stable pattern. Although this has been difficult to explain theoretically, neural network models optimized for WM typically also exhibit such dynamics. Here, we examine stable versus dynamic coding in neural data, classical models, and task-optimized networks. We review principled mathematical reasons for why classical models do not, while task-optimized models naturally do exhibit dynamic coding. We suggest an update to our understanding of WM maintenance, in which dynamic coding is a fundamental computational feature rather than an epiphenomenon.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Memória de Curto Prazo
/
Modelos Neurológicos
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article