eLife Assessment
This valuable study introduces a novel method for controlling generalization and interference in neural networks undergoing continual learning. The authors provide solid evidence that their parsimonious method performs better than online gradient descent in several continual learning situations while providing biologically plausible links to three-factor learning rules. However, empirical validation is limited to linear networks, which raises questions about the generality of the results in non-linear networks. While the work is interesting to theoretical and experimental neuroscientists, improving the article presentation by clearly defining terminology before using it and providing more details on the setup of the simulation experiments would be vital to make the article more accessible.