  Humans and most animals can learn new tasks without forgetting old ones.
However, training artificial neural networks (ANNs) on new tasks typically
cause it to forget previously learned tasks. This phenomenon is the result of
"catastrophic forgetting", in which training an ANN disrupts connection weights
that were important for solving previous tasks, degrading task performance.
Several recent studies have proposed methods to stabilize connection weights of
ANNs that are deemed most important for solving a task, which helps alleviate
catastrophic forgetting. Here, drawing inspiration from algorithms that are
believed to be implemented in vivo, we propose a complementary method: adding a
context-dependent gating signal, such that only sparse, mostly non-overlapping
patterns of units are active for any one task. This method is easy to
implement, requires little computational overhead, and allows ANNs to maintain
high performance across large numbers of sequentially presented tasks when
combined with weight stabilization. This work provides another example of how
neuroscience-inspired algorithms can benefit ANN design and capability.
