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. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Jitsev, J. (2014). Self-generated off-line memory reprocessing strongly improves generalization in a hierarchical recurrent neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8681 LNCS, pp. 659–666). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_83
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