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.
We identify representational structure in CA1 remapping that is reliable across brains.
We directly compare models of cognitive mapping to this representation in CA1.
Models based on local boundary distance and direction predict CA1 representation.
This approach reveals a biologically viable parameter space for model predictions.
Accurate models capture the correspondence of spatial codes across environments.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Guided by feedback from reviewers, we have added new analyses, revisions and expansions of the text (we provide a total of 24 new display items in the main figures and 13 in supplement) that focus in detail on the pattern of hippocampal remapping in our paradigm and provide a specific and thorough evaluation of competing theoretical models to explain this fundamental process in the brain. Based on these new and expanded results, we highlight the novel finding that accurate models of cognitive mapping predict the correspondence in spatial coding of a changing environment (i.e., remapping).
