Department of Psychology, University of Texas at Austin, Austin, Texas 78712.
Center for Learning and Memory, University of Texas at Austin, Austin, Texas 78712.
J Neurosci. 2021 Jan 27;41(4):726-738. doi: 10.1523/JNEUROSCI.0394-20.2020. Epub 2020 Nov 25.
Events that overlap with previous experience may trigger reactivation of existing memories. However, such reactivation may have different representational consequences within the hippocampal circuit. Computational theories of hippocampal function suggest that dentate gyrus and CA (DG/CA) are biased to differentiate highly similar memories, whereas CA may integrate related events by representing them with overlapping neural codes. Here, we tested whether the formation of differentiated or integrated representations in hippocampal subfields depends on the strength of memory reactivation during learning. Human participants of both sexes learned associations (AB pairs, either face-shape or scene-shape), and then underwent fMRI scanning while they encoded overlapping associations (BC shape-object pairs). Both before and after learning, participants were also scanned while viewing indirectly related elements of the overlapping memories (A and C images) in isolation. We used multivariate pattern analyses to measure reactivation of initial pair memories (A items) during overlapping pair (BC) learning, as well as learning-related representational change for indirectly related memory elements in hippocampal subfields. When prior memories were strongly reactivated during overlapping pair encoding, DG/CA and subiculum representations for indirectly related images (A and C) became less similar, consistent with pattern differentiation. Simultaneously, memory reactivation during new learning promoted integration in CA, where representations for indirectly related memory elements became more similar after learning. Furthermore, memory reactivation and subiculum representation predicted faster and more accurate inference (AC) decisions. These data show that reactivation of related memories during new learning leads to dissociable coding strategies in hippocampal subfields, in line with computational theories. The flexibility of episodic memory allows us to remember both the details that differentiate similar events and the commonalities among them. Here, we tested how reactivation of past experience during new learning promotes formation of neural representations that might serve these two memory functions. We found that memory reactivation during learning promoted formation of differentiated representations for overlapping memories in the dentate gyrus/CA and subiculum subfields of the hippocampus, while simultaneously leading to the formation of integrated representations of related events in subfield CA Furthermore, memory reactivation and subiculum representation predicted success when inferring indirect relationships among events. These findings indicate that memory reactivation is an important learning signal that influences how overlapping events are represented within the hippocampal circuit.
与先前经验重叠的事件可能会触发现有记忆的重新激活。然而,海马回路内的这种重新激活可能具有不同的表示后果。海马功能的计算理论表明,齿状回和 CA(DG/CA)偏向于区分高度相似的记忆,而 CA 可能通过用重叠的神经代码表示相关事件来整合这些事件。在这里,我们测试了海马亚区中形成差异化或整合表示的方式是否取决于学习过程中记忆重新激活的强度。来自不同性别的人类参与者学习关联(AB 对,面孔形状或场景形状),然后在他们编码重叠关联(BC 形状对象对)时进行 fMRI 扫描。在学习之前和之后,参与者还在单独观看重叠记忆的间接相关元素(A 和 C 图像)时进行扫描。我们使用多元模式分析来测量在重叠对(BC)学习期间初始对记忆(A 项)的重新激活,以及海马亚区中间接相关记忆元素的学习相关表示变化。当先前的记忆在重叠对编码期间被强烈重新激活时,DG/CA 和 subiculum 对间接相关图像(A 和 C)的表示变得不那么相似,与模式分化一致。同时,新学习期间的记忆重新激活促进了 CA 中的整合,其中间接相关记忆元素的表示在学习后变得更加相似。此外,记忆重新激活和 subiculum 表示预测了更快和更准确的推断(AC)决策。这些数据表明,在新学习期间,相关记忆的重新激活导致海马亚区中分离的编码策略,与计算理论一致。情景记忆的灵活性使我们能够记住区分相似事件的细节和它们之间的共同点。在这里,我们测试了新学习期间过去经验的重新激活如何促进形成可能服务于这两种记忆功能的神经表示。我们发现,学习期间的记忆重新激活促进了海马齿状回/CA 和 subiculum 亚区中重叠记忆的差异化表示的形成,同时导致了 CA 中亚区中相关事件的整合表示的形成。此外,记忆重新激活和 subiculum 表示预测了推断事件之间间接关系的成功。这些发现表明,记忆重新激活是一个重要的学习信号,它影响了重叠事件在海马回路内的表示方式。