Identifying representational structure in CA1 to benchmark theoretical models of cognitive mapping

Abstract

Decades of theoretical and empirical work have suggested the hippocampus instantiates some form of a cognitive map. Yet, tests of competing theories have been limited in scope and largely qualitative in nature. Here, we develop a novel framework to benchmark model predictions against observed neuronal population dynamics as animals navigate a series of geometrically distinct environments. In this task space, we show a representational structure in the dynamics of hippocampal remapping that generalizes across brains, discriminates between competing theoretical models, and effectively constrains biologically viable model parameters. With this approach, we find that accurate models capture the correspondence in spatial coding of a changing environment. The present dataset and framework thus serve to empirically evaluate and advance theories of cognitive mapping in the brain. HIGHLIGHTSO_LIWe identify representational structure in CA1 remapping that is reliable across brains. C_LIO_LIWe directly compare models of cognitive mapping to this representation in CA1. C_LIO_LIModels based on local boundary distance and direction predict CA1 representation. C_LIO_LIThis approach reveals a biologically viable parameter space for model predictions. C_LIO_LIAccurate models capture the correspondence of spatial codes across environments. C_LI