Topic: synthetic task-sequence paradigm

Continual learning (CL), the capability to acquire knowledge and skills over time without forgetting old ones, is fundamental to how animals survive in a non-stationary world. This ability has proven difficult to realize in artificial neural networks (ANNs) [1], which suffer from catastrophic forgetting of previously learned information. To understand this contrast, we sought to understand what aspects of a neural system and the tasks it learns are key to its CL performance. We developed a theory of deep, nonlinear feedforward networks sequentially learning multiple tasks and derived exact exp