Drifting codes within a stable coding scheme for working memory

Working memory (WM) is important to maintain information over short time periods to provide some stability in a constantly changing environment. However, brain activity is inherently dynamic, raising a challenge for maintaining stable mental states. To investigate the relationship between WM stability and neural dynamics, we used electroencephalography to measure the neural response to impulse stimuli during a WM delay. Multivariate pattern analysis revealed representations were both stable and dynamic: there was a clear difference in neural states between time-specific impulse responses, reflecting dynamic changes, yet the coding scheme for memorised orientations was stable. This suggests that a stable subcomponent in WM enables stable maintenance within a dynamic system. A stable coding scheme simplifies readout for WM-guided behaviour, whereas the low-dimensional dynamic component could provide additional temporal information. Despite having a stable subspace, WM is clearly not perfect—memory performance still degrades over time. Indeed, we find that even within the stable coding scheme, memories drift during maintenance. When averaged across trials, such drift contributes to the width of the error distribution.

工作记忆（Working Memory）能够在短时间内维持信息，为持续变化的外部环境提供一定稳定性，这一功能至关重要。然而，大脑活动本质上具有动态特性，这为维持稳定的心理状态带来了挑战。为探究工作记忆稳定性与神经动态之间的关联，我们采用脑电图（Electroencephalography）技术，在工作记忆延迟阶段测量了大脑对脉冲刺激的神经响应。多变量模式分析结果显示，神经表征兼具稳定性与动态性：不同时间点的脉冲响应所对应的神经状态存在显著差异，这反映了动态变化过程；但针对记忆方位的编码方案却保持稳定。这表明工作记忆中存在一个稳定的子组件，使其能够在动态系统中实现信息的稳定维持。稳定的编码方案能够简化工作记忆驱动行为的信息读取流程，而低维动态组件则可提供额外的时序信息。尽管工作记忆拥有稳定的子空间，但其功能并非完美无缺——记忆表现仍会随时间推移而衰退。事实上，我们发现即便在稳定的编码方案框架内，记忆在维持过程中仍会发生漂移。当对多次试验的数据取平均后，这类漂移会导致误差分布的宽度增加。