Big-Loop Recurrence within the Hippocampal System Supports Integration of Information across Episodes

Raphael Koster, Martin J. Chadwick, Yi Chen, David Berron, Andrea Banino, Emrah Düzel, Demis Hassabis, Dharshan Kumaran

Research output: Contribution to journalArticlepeer-review

35 Citations (SciVal)

Abstract

Recent evidence challenges the widely held view that the hippocampus is specialized for episodic memory, by demonstrating that it also underpins the integration of information across experiences. Contemporary computational theories propose that these two contrasting functions can be accomplished by big-loop recurrence, whereby the output of the system is recirculated back into the hippocampus. We use ultra-high-resolution fMRI to provide support for this hypothesis, by showing that retrieved information is presented as a new input on the superficial entorhinal cortex—driven by functional connectivity between the deep and superficial entorhinal layers. Further, the magnitude of this laminar connectivity correlated with inferential performance, demonstrating its importance for behavior. Our findings offer a novel perspective on information processing within the hippocampus and support a unifying framework in which the hippocampus captures higher-order structure across experiences, by creating a dynamic memory space from separate episodic codes for individual experiences. The hippocampus is central for storing distinct episodes, while also supporting integration across related episodes. Using ultra-high-resolution fMRI, Koster et al. provide evidence for a core computational principle (big-loop recurrence) that can account for these apparently conflicting hippocampal roles.

Original languageEnglish
Pages (from-to)1342-1354.e6
JournalNeuron
Volume99
Issue number6
DOIs
Publication statusPublished - 2018 Sep 19

Subject classification (UKÄ)

  • Neurosciences

Keywords

  • 7 Tesla
  • fMRI
  • hippocampus
  • MVPA
  • paired-associate inference task

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