Drifting codes within a stable coding scheme for working memory.,PLOS Biology

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Drifting codes within a stable coding scheme for working memory.
PLOS Biology ( IF 7.8 ) Pub Date : 2020-03-02 , DOI: 10.1371/journal.pbio.3000625
Michael J Wolff _{1,

2} , Janina Jochim ₃ , Elkan G Akyürek ₂ , Timothy J Buschman ₄ , Mark G Stokes _{1,

3}

Affiliation

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.

中文翻译：

在工作记忆的稳定编码方案中漂移代码。

工作记忆 (WM) 对于在短时间内保存信息以在不断变化的环境中提供一定的稳定性非常重要。然而，大脑活动本质上是动态的，这对维持稳定的精神状态提出了挑战。为了研究 WM 稳定性和神经动力学之间的关系，我们使用脑电图来测量 WM 延迟期间对脉冲刺激的神经反应。多变量模式分析显示表征既稳定又动态：特定时间脉冲响应之间的神经状态存在明显差异，反映了动态变化，但记忆方向的编码方案是稳定的。这表明 WM 中的稳定子组件可以实现动态系统内的稳定维护。稳定的编码方案简化了 WM 引导行为的读出，而低维动态分量可以提供额外的时间信息。尽管拥有稳定的子空间，WM 显然并不完美——内存性能仍然会随着时间的推移而下降。事实上，我们发现即使在稳定的编码方案中，记忆在维护过程中也会发生漂移。当对试验进行平均时，这种漂移会影响误差分布的宽度。

更新日期：2020-04-16

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