Using noise to compute error surfaces in connectionist networks: a novel means of reducing catastrophic forgetting.
In error-driven distributed feedforward networks, new information typically interferes, sometimes severely, with previously learned information. We show how noise can be used to approximate the error surface of previously learned information. By combining this approximated error surface with the error surface associated with the new information to be learned, the network's retention of previously learned items can be improved and catastrophic interference significantly reduced. Further, we show that the noise-generated error surface is produced using only first-derivative information and without recourse to any explicit error information.
French
Robert M
RM
Quantitative Psychology and Cognitive Science, Psychology Department, University of Liège, 4000 Liège, Belgium. rfrench@ulg.ac.be
Chater
Nick
N
D000818:Animals; D016477:Artifacts; D003198:Computer Simulation; D008568:Memory; D008569:Memory Disorders; D016571:Neural Networks, Computer