Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks.

ABSTRACT: A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-speci

SUBMITTER: Velez R 

PROVIDER: S-EPMC5690421 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature