In the mammalian brain, newly acquired memories depend on the hippocampus (HPC) for maintenance and recall, but over time, the neocortex takes over these functions, rendering memories HPC-independent. The process responsible for this transformation is called systems memory consolidation. Reactivation of a well-consolidated memory can trigger a temporary return to a HPC-dependent state, a phenomenon known as systems memory reconsolidation. The neural mechanisms underlying systems memory consolidation and reconsolidation are not well understood. Here, we propose a neural model based on well-documented mechanisms of synaptic plasticity and stability and describe a computational implementation that demonstrates the model's ability to account for a range of findings from the systems consolidation and reconsolidation literature. We derive several predictions from the computational model and suggest experiments that may test its validity.
Keywords: AMPA receptor exchange; artificial neural network; computational model; memory reconsolidation; neural plasticity.
© 2019 Wiley Periodicals, Inc.