Flexible Use of Limited Resources for Sequence Working Memory in Macaque Prefrontal Cortex

Siwei Li, Jingwen Chen, Cong Zhang, Shiming Tang, Yang Xie () and Liping Wang ()
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Siwei Li: Chinese Academy of Sciences, Institute of Neuroscience, Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, CAS Center for Excellence in Brain Science and Intelligence Technology
Jingwen Chen: Chinese Academy of Sciences, Institute of Neuroscience, Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, CAS Center for Excellence in Brain Science and Intelligence Technology
Cong Zhang: Chinese Academy of Sciences, Institute of Neuroscience, Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, CAS Center for Excellence in Brain Science and Intelligence Technology
Shiming Tang: Peking University School of Life Sciences and Peking-Tsinghua Center for Life Sciences
Yang Xie: Lingang Laboratory
Liping Wang: Chinese Academy of Sciences, Institute of Neuroscience, Key Laboratory of Brain Cognition and Brain-Inspired Intelligence Technology, CAS Center for Excellence in Brain Science and Intelligence Technology

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract Our brain is remarkably limited in how many items it can hold simultaneously, but it can also represent unbounded novel items through generalization. How the brain rationally uses limited resources in working memory (WM) remains unexplored. We investigated mechanisms of WM resource allocation using calcium imaging and electrophysiological recording in the prefrontal cortex of monkeys performing sequence WM (SWM) tasks. We found that changes in the neural representation of SWM, including geometry, generalizable and separate rank subspaces, reflected WM load. SWM resources, represented by neurons’ signal strength and spatial tuning projected onto each rank subspace, were shared flexibly between ranks. Crucially, the prefrontal cortex dynamically utilized shared tuning neurons to ensure generalization, while engaging disjoint and spatially shifted neurons to minimize interference, thus achieving a trade-off between behavioral and neural costs within capacity. The allocated resources can predict monkeys’ behavior. Thus, the geometry of compositionality underlies the flexible use of limited resources in SWM.

Date: 2025
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DOI: 10.1038/s41467-025-65380-0

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