Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization.

ABSTRACT: Humans and most animals can learn new tasks without forgetting old ones. However, training artificial neural networks (ANNs) on new tasks typically causes them to forget previously learned tasks. This phenomenon is the result of "catastrophic forgetting," in which training an ANN disrupts connection weights that were important for solving previous tasks, degrading task performance. Several recent studies have proposed methods to stabilize connection weights of ANNs that are deemed most important for solving a task, which helps alleviate catastrophic forgetting. Here, drawing inspiration from algorithms that are believed to be implemented in vivo, we propose a complementary method: adding a context-dependent gating signal, such that only sparse, mostly nonoverlapping patterns of units are a

SUBMITTER: Masse NY 

PROVIDER: S-EPMC6217392 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature