Category learning disentangles representation of trial events in hippocampus CA1

Abstract

The hippocampus (HC) is known to encode task-relevant variables, including latent ones, capturing and parsing critical information from sequences into episodes. This is thought to be the basis to form a cognitive map of possible abstract states beyond mere perceptual details, akin a state machine, with predictive value, contributing to an internal world model. However, its specific role in categorization tasks in general, where learning a category may depend on independent, unordered (non-sequential) experienced examples, remains unclear. Here, we investigate CA1 population coding during a categorization paradigm in mice in combination with calcium imaging at different stages of category training with interleaved generalization tests. Our results reveal that hippocampal coding changes critically throughout the different stages of categorization training. Specifically, the disentangling of choice and outcome variables emerges as factorized, abstract, representations and fundamentally distinguishes simple discrimination from categorization training. Furthermore, this factorized geometry relates to improved behavioral performance. Our findings suggest that trial encoding on HC adapts in response to the category structure presented by representing critical events into the appropriate computational format to support generalization of category membership.