Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it in certain regimes.

中文翻译：

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动态记忆的连续吸引子

情节记忆具有动态特性：当我们回忆过去的情节时，我们不仅会检索它们的内容，还会检索它们的时间结构。哺乳动物海马体中的重播现象提供了这种时间动态的一个显着例子。然而，大多数记忆的定量模型将记忆视为静态配置，忽略了检索过程的时间展开。在这里，我们引入了一个连续的吸引子网络模型，它在突触连接中具有依赖于记忆的不对称组件，它自发地打破了记忆配置的平衡并产生动态检索。通过解析计算和数值模拟对模型的详细分析表明，它可以稳健地检索多个动态记忆，并且此功能在很大程度上与其实现的细节无关。通过计算存储容量，我们表明动态组件不会损害内存容量，甚至可以在某些情况下增强它。