Hippocampo-neocortical interaction as compressive retrieval-augmented generation (opens in new tab)

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 effici...