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Dual-Network Memory Model For Temporal Sequences
2014
Zenodo
In neural networks, when new patters are learned by a network, they radically interfere with previously stored patterns. This drawback is called catastrophic forgetting. We have already proposed a biologically inspired dual-network memory model which can much reduce this forgetting for static patterns. In this model, information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network using pseudopatterns. Because temporal sequence learning is
doi:10.5281/zenodo.1336104
fatcat:5k7isd2qafh4lmummkemenbwzq