Remote memory in a Bayesian model of context fear conditioning (BaconREM)

Abstract

Here, we propose a model of remote memory (BaconREM), which is an extension of a previously published Bayesian model of context fear learning (BACON) that accounts for many aspects of recently learned context fear. BaconREM simulates most known phenomenology of remote context fear as studied in rodents and makes new predictions. In particular, it predicts the well-known observation that fear that was conditioned to a recently encoded context becomes hippocampus-independent and shows much-enhanced generalization (“hyper-generalization”) when systems consolidation occurs (i.e., when memory becomes remote). However, the model also predicts that there should be circumstances under which the generalizability of remote fear may not increase or even decrease. It also predicts the established finding that a “reminder” exposure to a feared context can abolish hyper-generalization while at the same time making remote fear again hippocampus-dependent. This observation has in the past been taken to suggest that reminders facilitate access to detail memory that remains permanently in the hippocampus even after systems consolidation is complete. However, the present model simulates this result even though it totally moves all the contextual memory that it retains to the neo-cortex when context fear becomes remote.