A Timescale-Adaptive RNN for Rule-Guided Reorganization of Sequence Working Memory

Sequence working memory (SWM) is not merely a system for temporarily maintaining information, but an active computational workspace in which stored representations can be reorganized according to task demands. Recent recordings from primate frontal cortex revealed rank-content factorized representations and additional transient subspaces associated with sequence reordering, but the underlying circuit mechanisms remain unclear. Here, we show that a recurrent neural network with cue-triggered modulation of neuronal integration time constants can support flexible SWM manipulation while recapitulating key experimentally observed representational and dynamical signatures of SWM. After training on a rule-guided SWM manipulation task, the network developed a content-independent scaffold of ordinal-rank subspaces that served as reusable slots for different items. Before rule presentation, item-rank mappings were maintained; the forward rule preserved these mappings, whereas the backward rule reassigned items between rank slots. Item information was transiently routed into two additional subspaces during reordering, consistent with a buffering role. Comparisons across architectures and timescale parameters confirmed the importance of cue-triggered timescale modulation for learning this factorized memory organization. Three-item experiments further provided initial evidence that learned operations could be compositionally reused to support multi-step working-memory manipulation. These results suggest a concrete recurrent mechanism for flexible item–rank reorganization and provide mechanistic insight into how mental programming may operate through computations over factorized content and ordinal-rank representations.