Self-generated Off-line Memory Reprocessing Strongly Improves Generalization in a Hierarchical Recurrent Neural Network

Strong experimental evidence suggests that cortical memory traces are consolidated during off-line memory reprocessing that occurs in the off-line states of sleep or waking rest. It is unclear, what plasticity mechanisms are involved in this process and what changes are induced in the network in the off-line regime. Here, we examine a hierarchical recurrent neural network that performs unsupervised learning on natural face images of different persons. The proposed network is able to self-generate memory replay while it is decoupled from external stimuli. Remarkably, the recognition performance is tremendously boosted after this off-line regime specifically for the novel face views that were not shown during the initial learning. This effect is independent of synapse-specific plasticity, relying completely on homeostatic regulation of intrinsic excitability. Comparing a purely feed-forward network configuration with the full version reveals a substantially stronger boost in recognition performance for the fully recurrent network architecture after the off-line regime.