Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom.
Proc Natl Acad Sci U S A. 2024 Jul 23;121(30):e2403648121. doi: 10.1073/pnas.2403648121. Epub 2024 Jul 17.
Theoretical models conventionally portray the consolidation of memories as a slow process that unfolds during sleep. According to the classical Complementary Learning Systems theory, the hippocampus (HPC) rapidly changes its connectivity during wakefulness to encode ongoing events and create memory ensembles that are later transferred to the prefrontal cortex (PFC) during sleep. However, recent experimental studies challenge this notion by showing that new information consistent with prior knowledge can be rapidly consolidated in PFC during wakefulness and that PFC lesions disrupt the encoding of congruent events in the HPC. The contributions of the PFC to memory encoding have therefore largely been overlooked. Moreover, most theoretical frameworks assume random and uncorrelated patterns representing memories, disregarding the correlations between our experiences. To address these shortcomings, we developed a HPC-PFC network model that simulates interactions between the HPC and PFC during the encoding of a memory (awake stage), and subsequent consolidation (sleeping stage) to examine the contributions of each region to the consolidation of novel and congruent memories. Our results show that the PFC network uses stored memory "schemas" consolidated during previous experiences to identify inputs that evoke congruent patterns of activity, quickly integrate it into its network, and gate which components are encoded in the HPC. More specifically, the PFC uses GABAergic long-range projections to inhibit HPC neurons representing input components correlated with a previously stored memory "schema," eliciting sparse hippocampal activity during exposure to congruent events, as it has been experimentally observed.
理论模型通常将记忆的巩固描绘为一个在睡眠中展开的缓慢过程。根据经典的互补学习系统理论，海马体（HPC）在清醒时会迅速改变其连接，以编码正在发生的事件，并创建记忆集合，这些集合随后在睡眠中转移到前额叶皮层（PFC）。然而，最近的实验研究挑战了这一观点，表明与先前知识一致的新信息可以在清醒时迅速在 PFC 中巩固，并且 PFC 损伤会破坏 HPC 中与一致事件的编码。因此，PFC 对记忆编码的贡献在很大程度上被忽视了。此外，大多数理论框架假设记忆代表随机且不相关的模式，而忽略了我们经历之间的相关性。为了解决这些缺点，我们开发了一个 HPC-PFC 网络模型，该模型模拟了记忆编码（清醒阶段）和随后巩固（睡眠阶段）期间 HPC 和 PFC 之间的相互作用，以检查每个区域对新记忆和一致记忆巩固的贡献。我们的结果表明，PFC 网络使用在先前经验中巩固的存储记忆“模式”来识别引发一致活动模式的输入，快速将其整合到其网络中，并确定哪些组件被编码在 HPC 中。更具体地说，PFC 使用 GABA 能长程投射来抑制代表与先前存储的记忆“模式”相关的输入组件的 HPC 神经元，从而在暴露于一致事件时引发稀疏的海马体活动，正如实验观察到的那样。
