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<p>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 <i>compress</i> episodic memories into hippocampus and to <i>consolidate</i> 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.</p>

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Hippocampo-neocortical interaction as compressive retrieval-augmented generation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月25日

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  • Eleanor Spens,
  • Neil Burgess

摘要

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.