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

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.