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Offline generative network reconfiguration guides insight-like accelerated learning by assimilation into schema in rats.

Created on 11 Sep 2026

Authors

Baburam Bhattarai, George Dragoi

Published in

Nature communications. Volume 17. Issue 1. Sep 03, 2026. Epub Sep 03, 2026.

Abstract

Complex cross-modal de-novo associative learning generally requires numerous encoding exposures, but acquisition of an underlying mental schema of associative abstract rules enables insight-like accelerated new learning. Reports indicate that post-encoding sleep/rest offline epochs play an active role in insight learning, but the supporting neuronal ensemble mechanisms remained elusive. We developed a complex cross-modal learning task where six cue-place paired-associations (PAs) learned by male rats over weeks created a mental schema that enabled rapid within-day acquisition of 3-6 novel PAs. Simultaneous electrophysiological recording of hippocampus (HPC)-medial prefrontal (mPFC) ensembles across exploration-rest-sleep states indicated that accelerated learning of new PAs combined inferential activation of HPC map-based cue-place abstract associations and offline generative network reconfiguration in coordination with mPFC generalized outcome coding. HPC network reconfiguration during post-encoding sleep/rest predicted insight-like accelerated learning of 3-6 new PAs that consolidated and transferred rapidly via ripple-coordinated cell-assembly co-activation to mPFC. HPC ripple disruption during post-encoding sleep/rest prevented schema-based accelerated learning. Our findings reveal that map-based associative inference via offline predictive HPC-mPFC generative network reconfiguration supports insight-like accelerated learning by assimilation into schema and systems consolidation.

PMID:
42722686
Bibliographic data and abstract were imported from PubMed on 11 Sep 2026.

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