Working memory (WM) is a fundamental aspect of cognition. WM maintenance is classically thought to rely on stable patterns of neural activities. However, recent evidence shows that neural population activities during WM maintenance undergo dynamic variations before settling into a stable pattern. Although this has been difficult to explain theoretically, neural network models optimized for WM typically also exhibit such dynamics. Here, we examine stable versus dynamic coding in neural data, classical models, and task-optimized networks. We review principled mathematical reasons for why classical models do not, while task-optimized models naturally do exhibit dynamic coding. We suggest an update to our understanding of WM maintenance, in which dynamic coding is a fundamental computational feature rather than an epiphenomenon.

中文翻译：

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工作记忆动态编码的计算基础


工作记忆（WM）是认知的一个基本方面。传统上认为 WM 维持依赖于神经活动的稳定模式。然而，最近的证据表明，WM 维持期间的神经群体活动在进入稳定模式之前会经历动态变化。尽管这在理论上很难解释，但针对 WM 优化的神经网络模型通常也表现出这种动态。在这里，我们研究神经数据、经典模型和任务优化网络中的稳定编码与动态编码。我们回顾了为什么经典模型不这样做的原则性数学原因，而任务优化模型自然地确实表现出动态编码。我们建议更新我们对 WM 维护的理解，其中动态编码是一种基本的计算特征，而不是一种附带现象。