Self-refreshing memory in artificial neural networks: learning temporal sequences without catastrophic forgetting

While humans forget gradually, highly distributed connectionist networks forget catastrophically: newly learned information often completely erases previously learned information. This is not just implausible cognitively, but disastrous practically. However, it is not easy in connectionist cognitive modelling to keep away from highly distributed neural networks, if only because of their ability to generalize. A realistic and effective system that solves the problem of catastrophic interference in sequential learning of `static' (i.e. non-temporally ordered) patterns has been proposed recently (Robins 1995, Connection Science, 7: 123

• Type: Journal
• Year: 2004
• Where: CONNECTION
• Authors: Bernard Ans, Stephane Rousset, Robert M. French, Serban C. Musca
• Keywords: CONNECTION 2004, Connection Science, Connectionist Networks, Temporal Sequence