A Computational Model of Systems Memory Consolidation and Reconsolidation

TL;DR

This paper presents a neural computational model explaining how memories transition from hippocampus-dependent to neocortex-dependent over time and how reactivation can temporarily revert this process, aligning with empirical findings.

Contribution

It introduces a novel computational model based on synaptic plasticity mechanisms that accounts for systems memory consolidation and reconsolidation phenomena.

Findings

Model reproduces key experimental observations

Predicts new experimental outcomes

Suggests specific neural mechanisms involved

Abstract

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.