Task representations in neural networks trained to perform many cognitive tasks.

ABSTRACT: The brain has the ability to flexibly perform many tasks, but the underlying mechanism cannot be elucidated in traditional experimental and modeling studies designed for one task at a time. Here, we trained single network models to perform 20 cognitive tasks that depend on working memory, decision making, categorization, and inhibitory control. We found that after training, recurrent units can develop into clusters that are functionally specialized for different cognitive processes, and we introduce a simple yet effective measure to quantify relationships between single-unit neural representations of tasks. Learning often gives rise to compositionality of task representations, a critical feature for cognitive flexibility, whereby one task can be performed by recombining instructions for ot

SUBMITTER: Yang GR 

PROVIDER: S-EPMC11549734 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature