Department of Psychology, New York University, 6 Washington Place, New York, NY 10003, USA.
Department of Psychology, New York University, 6 Washington Place, New York, NY 10003, USA.
Curr Biol. 2017 Aug 7;27(15):2307-2317.e5. doi: 10.1016/j.cub.2017.06.057. Epub 2017 Jul 20.
Across the domains of spatial navigation and episodic memory, the hippocampus is thought to play a critical role in disambiguating (pattern separating) representations of overlapping events. However, it is not fully understood how and why hippocampal patterns become separated. Here, we test the idea that event overlap triggers a "repulsion" among hippocampal representations that develops over the course of learning. Using a naturalistic route-learning paradigm and spatiotemporal pattern analysis of human fMRI data, we found that hippocampal representations of overlapping routes gradually diverged with learning to the point that they became less similar than representations of non-overlapping events. In other words, the hippocampus not only disambiguated overlapping events but formed representations that "reversed" the objective similarity among routes. This finding, which was selective to the hippocampus, is not predicted by standard theoretical accounts of pattern separation. Critically, because the overlapping route stimuli that we used ultimately diverged (so that each route contained overlapping and non-overlapping segments), we were able to test whether the reversal effect was selective to the overlapping segments. Indeed, once overlapping routes diverged (eliminating spatial and visual similarity), hippocampal representations paradoxically became relatively more similar. Finally, using a novel analysis approach, we show that the degree to which individual hippocampal voxels were initially shared across route representations was predictive of the magnitude of learning-related separation. Collectively, these findings indicate that event overlap triggers a repulsion of hippocampal representations-a finding that provides critical mechanistic insight into how and why hippocampal representations become separated.
在空间导航和情景记忆的各个领域，海马体被认为在消除（模式分离）重叠事件的表示方面起着关键作用。然而，人们并不完全理解海马体模式是如何以及为什么变得分离的。在这里，我们检验了这样一种观点，即事件重叠会引发海马体表示之间的“排斥”，这种排斥在学习过程中发展。使用自然主义的路线学习范式和人类 fMRI 数据的时空模式分析，我们发现，重叠路线的海马体表示随着学习逐渐发散，以至于它们变得不那么相似，而不是非重叠事件的表示。换句话说，海马体不仅消除了重叠事件，而且形成了“反转”路线之间客观相似性的表示。这一发现是选择性的，与标准的模式分离理论解释不一致。关键的是，因为我们使用的重叠路线刺激最终会发散（这样每条路线都包含重叠和非重叠的部分），所以我们能够测试这种反转效应是否只针对重叠部分。事实上，一旦重叠路线发散（消除空间和视觉相似性），海马体的表示反而变得相对更相似。最后，使用一种新的分析方法，我们表明，个体海马体体素在初始时在路线表示中共享的程度与学习相关分离的程度相关。总的来说，这些发现表明，事件重叠会引发海马体表示的排斥，这一发现为理解海马体表示如何以及为什么变得分离提供了关键的机制见解。
