The hippocampal formation as a hierarchical generative model supporting generative replay and continual learning

We advance a novel computational theory of the hippocampal formation as a hierarchical generative model that organizes sequential experiences, such as rodent trajectories during spatial navigation, into coherent spatiotemporal contexts. We propose that to make this possible, the hippocampal generative model is endowed with strong inductive biases to pattern-separate individual items of experience (at the first hierarchical layer), organize them into sequences (at the second layer) and then cluster them into maps (at the third layer). This theory entails a novel characterization of hippocampal reactivations as generative replay: the offline resampling of fictive sequences from the generative model, for the sake of continual learning of multiple sequential experiences. Our experiments show that the hierarchical model using generative replay is able to learn and retain efficiently multiple spatial navigation trajectories, organizing them into separate spatial maps. Furthermore, it reproduces flexible aspects of hippocampal dynamics that have been challenging to explain within existing frameworks. This theory reconciles multiple roles of the hippocampal formation in map-based navigation, episodic memory and imagination.