Author
Listed:
- 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
University of Chinese Academy of Sciences)
- 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
University of Chinese Academy of Sciences)
- 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
IDG/McGovern Institute for Brain Research at Peking University)
- 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
University of Chinese Academy of Sciences
Shanghai Key Laboratory of Clinical and Translational Brain-Computer Interface Research
Fudan University, Shanghai Academy of Natural Sciences (SANS))
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.
Suggested Citation
Siwei Li & Jingwen Chen & Cong Zhang & Shiming Tang & Yang Xie & Liping Wang, 2025.
"Flexible Use of Limited Resources for Sequence Working Memory in Macaque Prefrontal Cortex,"
Nature Communications, Nature, vol. 16(1), pages 1-18, December.
Handle:
RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-65380-0
DOI: 10.1038/s41467-025-65380-0
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