We present a deep network-based model of the associative memory functions of the hippocampus. The proposed network architecture has two key modules: (1) an autoencoder module which represents the forward and backward projections of the cortico-hippocampal projections and (2) a module that computes familiarity of the stimulus and implements hill-climbing over the familiarity which represents the dynamics of the loops within the hippocampus. The proposed network is used in two simulation studies. In the first part of the study, the network is used to simulate image pattern completion by autoassociation under normal conditions. In the second part of the study, the proposed network is extended to a heteroassociative memory and is used to simulate picture naming task in normal and Alzheimer’s disease (AD) conditions. The network is trained on pictures and names of digits from 0 to 9. The encoder layer of the network is partly damaged to simulate AD conditions. As in case of AD patients, under moderate damage condition, the network recalls superordinate words (“odd” instead of “nine”). Under severe damage conditions, the network shows a null response (“I don’t know”). Neurobiological plausibility of the model is extensively discussed.

中文翻译：

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正常和阿尔茨海默病条件下海马记忆功能的基于深度网络的模型

我们提出了一个基于深度网络的海马联想记忆功能模型。所提出的网络架构有两个关键模块：（1）一个自动编码器模块，它表示皮质海马投影的前向和后向投影；（2）一个计算刺激熟悉度并在代表的熟悉度上实现爬山的模块海马体内环路的动态。所提出的网络用于两项模拟研究。在研究的第一部分中，网络用于在正常条件下通过自动关联来模拟图像模式完成。在研究的第二部分中，所提出的网络被扩展到异联想记忆，并用于模拟正常和阿尔茨海默病（AD）条件下的图片命名任务。该网络接受图片和 0 到 9 数字名称的训练。网络的编码器层被部分损坏以模拟 AD 条件。与 AD 患者一样，在中度损伤的情况下，网络会回忆起上位词（“奇数”而不是“九”）。在严重损坏的情况下，网络会显示空响应（“我不知道”）。该模型的神经生物学合理性得到了广泛讨论。