Integration of new information in memory: new insights from a complementary learning systems perspective.

ABSTRACT: According to complementary learning systems theory, integrating new memories into the neocortex of the brain without interfering with what is already known depends on a gradual learning process, interleaving new items with previously learned items. However, empirical studies show that information consistent with prior knowledge can sometimes be integrated very quickly. We use artificial neural networks with properties like those we attribute to the neocortex to develop an understanding of the role of consistency with prior knowledge in putatively neocortex-like learning systems, providing new insights into when integration will be fast or slow and how integration might be made more efficient when the items to be learned are hierarchically structured. The work relies on deep linear networks

SUBMITTER: McClelland JL 

PROVIDER: S-EPMC7209926 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature