A Generative Model of Memory Construction and Consolidation

Abstract

Episodic memories are (re)constructed, combining unique features with familiar schemas, share neural substrates with imagination, and show schema-based distortions that increase with consolidation. Here we present a computational model in which hippocampal replay (from an autoassociative network) trains generative models (variational autoencoders) in neo-cortex to (re)create sensory experiences via latent variable representations in entorhinal, medial prefrontal, and anterolateral temporal cortices. Simulations show effects of memory age and hippocampal lesions in agreement with previous models, but also provide mechanisms for se-mantic memory, imagination, episodic future thinking, relational inference, and schema-based distortions including boundary extension. The model explains how unique sensory and predict-able conceptual or schematic elements of memories are stored and reconstructed by efficiently combining both hippocampal and neocortical systems, optimising the use of limited hippocam-pal storage for new and unusual information. Overall, we believe hippocampal replay training neocortical generative models provides a comprehensive account of memory construction, ima-gination and consolidation.