Spens & Burgess (2026)

Hippocampo-neocortical interaction as compressive retrieval-augmented generation

Nature Communications, 17(1)

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Abstract

Abstract Many aspects of learning, memory, and problem solving involve interplay between episodic (hippocampal) and semantic (neocortical) systems, but the neural mechanisms supporting this are unclear. We present a computational model in which sequential experiences are encoded in hippocampus in compressed form and replayed to train a neocortical generative network. This network captures the gist of specific episodes and extracts statistical patterns that generalise to new situations, enabling efficient reconstruction of the past and prediction of the future. The two systems interact during encoding, recall and problem solving, with the hippocampus retrieving relevant episodic information into working memory as a basis for generation using the ‘general knowledge’ of the neocortical network. We simulate this interaction as ‘retrieval-augmented generation’, with the addition of mechanisms to compress episodic memories into hippocampus and to consolidate them into neocortex. The model explains changes to memories over time, including schema-based distortions, and shows how episodic and semantic memory contribute to problem solving.

Reference

Spens, E., & Burgess, N. (2026). Hippocampo-neocortical interaction as compressive retrieval-augmented generation. Nature Communications, 17(1). https://doi.org/10.1038/s41467-026-74357-6

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Schema Application Representational Structures

Measured in AI

For information held in the model's trained weights, researchers fine-tuned Mistral-7B on Bartlett's "War of the Ghosts" after giving it background training on one of five Wikipedia topics; its recalls drifted toward that background topic with topic-related intrusions, matching Bartlett's and Bergman and Roediger's findings only qualitatively, in a research model rather than a deployed assistant