Neural-network simulations of memory consolidation and reconsolidation

TL;DR

This paper presents a neural network model simulating how memories are initially dependent on the hippocampus and later transferred to the neocortex, including the effects of reactivation on hippocampal dependence.

Contribution

It introduces a computational model that incorporates recent synaptic plasticity mechanisms to explain and predict memory consolidation and reconsolidation processes.

Findings

Model replicates hippocampus-dependent memory initially

Reactivation induces temporary hippocampal dependence

Predicts specific synaptic changes during consolidation

Abstract

In the mammalian brain newly acquired memories depend on the hippocampus for maintenance and recall, but over time these functions are taken over by the neocortex through a process called systems consolidation. However, reactivation of a consolidated memory can induce a brief period of temporary hippocampus-dependence, followed by return to hippocampus-independence. Here we present a computational model that uses simulation of recently described mechanisms of synaptic plasticity to account for findings from the systems consolidation/reconsolidation literature and to make predictions for future research.