Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

ABSTRACT: A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules

SUBMITTER: Ellefsen KO 

PROVIDER: S-EPMC4383335 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature