# 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.

## Key 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.

---
Source: https://tomesphere.com/paper/1703.01357