This work examines plasticity: a model's ability to keep learning as its tasks change.
The experiments focus on how components are selected for reuse or replacement. The question is whether task relevance can provide a more useful selection signal than a measure based only on connection strength or activity.
The work includes small demonstrations and comparisons. It is an investigation of a possible selection rule, not an established solution to plasticity loss.