  In the mammalian brain, newly acquired memories depend on the hippocampus for
maintenance and recall, but over time the neocortex takes over these functions,
rendering memories hippocampus-independent. The process responsible for this
transformation is called systems memory consolidation. However, reactivation of
a well-consolidated memory can trigger a temporary return to a
hippocampus-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.
