Humans possess a finely tuned sense of uncertainty that helps anticipate potential errors, vital for adaptive behavior and survival. However, the underlying neural mechanisms remain unclear. This study applies moment neural networks (MNNs) to explore the neural mechanism of uncertainty quantification in working memory (WM). The MNN captures nonlinear coupling of the first two moments in spiking neural networks (SNNs), identifying firing covariance as a key indicator of uncertainty in encoded information. Trained on a WM task, the model demonstrates coding precision and uncertainty quantification comparable to human performance. Analysis reveals a link between the probabilistic and sampling-based coding for uncertainty representation. Transferring the MNN's weights to an SNN replicates these results. Furthermore, the study provides testable predictions demonstrating how noise and heterogeneity enhance WM performance, highlighting their beneficial role rather than being mere biological byproducts. These findings offer insights into how the brain effectively manages uncertainty with exceptional accuracy.

翻译：人类具备精细调节的不确定性感知能力，这有助于预测潜在错误，对适应性行为和生存至关重要。然而，其潜在的神经机制尚不明确。本研究应用矩神经网络（MNNs）探索短时记忆（WM）中不确定性量化的神经机制。MNN捕捉了脉冲神经网络（SNNs）中前两阶矩的非线性耦合，识别出放电协方差作为编码信息不确定性的关键指标。通过在WM任务上进行训练，该模型展现出与人类表现相当的编码精度和不确定性量化能力。分析揭示了不确定性表征中概率编码与基于采样的编码之间的关联。将MNN的权重迁移至SNN可复现这些结果。此外，本研究提供了可验证的预测，证明噪声和异质性如何提升WM性能，突显了其有益作用而非仅仅是生物副产物。这些发现为理解大脑如何以卓越的准确性有效管理不确定性提供了新的见解。