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The computational foundations of dynamic coding in working memory.

Published version
Peer-reviewed

Repository DOI


Type

Article

Change log

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.

Description

Keywords

5203 Clinical and Health Psychology, 5204 Cognitive and Computational Psychology, 52 Psychology, Neurosciences, 1.2 Psychological and socioeconomic processes, 1.1 Normal biological development and functioning, 1 Underpinning research

Journal Title

Trends Cogn Sci

Conference Name

Journal ISSN

1364-6613
1879-307X

Volume Title

Publisher

Elsevier BV
Sponsorship
Wellcome Trust (212262/Z/18/Z)
Human Frontier Science Program (HFSP) (RGP0044/2018)
Wellcome Trust (215909/Z/19/Z)
MRC (MC_UU_00030/7)