2026/02/16 by Adedamola Onih, Xinran Shen, Lida Pentousi +2 · 2 voices · 1 citation
Biochemistry, Genetics and Molecular Biology · Neuroscience · #Animal Vocal Communication and Behavior #ENCODE #Hippocampus #Memory and Neural Mechanisms #Modularity (biology) #Neural dynamics and brain function #Neural substrate #Population #Sensory system #Sequence (biology) #Sequence learning #Unsupervised learning
paper · pdf · doi:10.64898/2026.02.14.705916
published in bioRxiv (Cold Spring Harbor Laboratory) (Cold Spring Harbor Laboratory)
openalex publication_date 2026/02/16 · openalex created_date 2026/02/17 · openalex updated_date 2026/07/14
Abstract Statistical learning (SL) allows organisms to infer latent structure from sensory input without instruction, feedback, or reward, yet how the brain accomplishes such abstract, unsupervised learning remains unknown. Here we show that mice, like humans, rapidly acquire multiple forms of statistical structure, including event frequency, sequence identity, and abstract structural rules, and that the hippocampus is essential for this capacity. Pupil dynamics provided a cross-species, implicit readout of expectation formation, revealing spontaneous sensitivity to these regularities during passive listening. In mice, pharmacological and temporally precise optogenetic inactivation of dorsal CA1 abolished all learning-related pupil signatures without affecting baseline pupil size, target-evoked responses, or task performance, demonstrating a causal requirement for the hippocampus in forming and updating internal models of sensory structure. High-density recordings further revealed that dCA1 ensembles track evolving statistical contexts while dynamically reorganising population activity into subspaces that separately encode sensory features and abstract rules, enabling generalisation across distinct but structurally equivalent sequences. Together, these results identify the hippocampus as a critical neural substrate for latent abstract structure learning and offer a mechanistic account of how internal models emerge from unsupervised experience.