Original paper
Forgetting Leads to Chaos in Attractor Networks
Abstract
Attractor networks are an influential theory for memory storage in brain systems. This theory has recently been challenged by the observation of strong temporal variability in neuronal recordings during memory tasks. In this work, we study a sparsely connected attractor network where memories are learned according to a Hebbian synaptic plasticity rule. After recapitulating known results for the continuous, sparsely connected Hopfield model, we...
Paper Details
Title
Forgetting Leads to Chaos in Attractor Networks
Published Date
Jan 27, 2023
Journal
Volume
13
Issue
1