We present the context-unified encoding (CUE) model, a large-scale spiking neural network model of human memory. It combines and integrates activity-based short-term memory (STM) with weight-based long-term memory. The implementation with spiking neurons ensures biological plausibility and allows for predictions on the neural level. At the same time, the model produces behavioral outputs that have been matched to human data from serial and free recall experiments. In particular, well-known results such as primacy, recency, transposition error gradients, and forward recall bias have been reproduced with good quantitative matches. Additionally, the model accounts for the Hebb repetition effect. The CUE model combines and extends the ordinal serial encoding model, a spiking neuron model of STM, and the temporal context model, a mathematical memory model matching free recall data. To implement the modification of the required association matrices, a novel learning rule, the association matrix learning rule, is derived that allows for one-shot learning without catastrophic forgetting. Its biological plausibility is discussed and it is shown that it accounts for changes in neural firing observed in human recordings from an association learning experiment. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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CUE：短期和长期记忆的统一尖峰神经元模型。

我们提出了上下文统一编码 (CUE) 模型，这是一种人类记忆的大规模尖峰神经网络模型。它结合并整合了基于活动的短期记忆 (STM) 和基于体重的长期记忆。尖峰神经元的实现确保了生物学的合理性，并允许在神经层面上进行预测。同时，该模型产生的行为输出与来自连续和自由回忆实验的人类数据相匹配。特别是，众所周知的结果，如首要性、新近度、转置误差梯度和前向召回偏差，已经以良好的定量匹配重现。此外，该模型还考虑了 Hebb 重复效应。CUE模型结合并扩展了序数串行编码模型、STM的尖峰神经元模型和时间上下文模型，匹配自由回忆数据的数学记忆模型。为了实现所需关联矩阵的修改，导出了一种新的学习规则，即关联矩阵学习规则，它允许一次性学习而不会出现灾难性遗忘。讨论了它的生物学合理性，并表明它可以解释关联学习实验中人类记录中观察到的神经放电变化。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。讨论了它的生物学合理性，并表明它可以解释关联学习实验中人类记录中观察到的神经放电变化。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。讨论了它的生物学合理性，并表明它可以解释关联学习实验中人类记录中观察到的神经放电变化。（PsycInfo 数据库记录 (c) 2020 APA，保留所有权利）。