PT  - JOURNAL ARTICLE
AU  - Yang, Guangyu Robert
AU  - Song, H. Francis
AU  - Newsome, William T.
AU  - Wang, Xiao-Jing
TI  - Clustering and compositionality of task representations in a neural network trained to perform many cognitive tasks
AID  - 10.1101/183632
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 183632
4099  - http://biorxiv.org/content/early/2017/09/01/183632.short
4100  - http://biorxiv.org/content/early/2017/09/01/183632.full
AB  - A neural system 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 a single network model to perform 20 cognitive tasks that may involve working memory, decision-making, categorization and inhibitory control. We found that after training, recurrent units developed into clusters that are functionally specialized for various cognitive processes. We introduce a measure to quantify relationships between single-unit neural representations of tasks, and report five distinct types of such relationships that can be tested experimentally. Surprisingly, our network developed compositionality of task representations, a critical feature for cognitive flexibility, whereby one task can be performed by recombining instructions for other tasks. Finally, we demonstrate how the network could learn multiple tasks sequentially. This work provides a computational platform to investigate neural representations of many cognitive tasks.