  The ability to sequentially learn multiple tasks without forgetting is a key
skill of biological brains, whereas it represents a major challenge to the
field of deep learning. To avoid catastrophic forgetting, various continual
learning (CL) approaches have been devised. However, these usually require
discrete task boundaries. This requirement seems biologically implausible and
often limits the application of CL methods in the real world where tasks are
not always well defined. Here, we take inspiration from neuroscience, where
sparse, non-overlapping neuronal representations have been suggested to prevent
catastrophic forgetting. As in the brain, we argue that these sparse
representations should be chosen on the basis of feed forward
(stimulus-specific) as well as top-down (context-specific) information. To
implement such selective sparsity, we use a bio-plausible form of hierarchical
credit assignment known as Deep Feedback Control (DFC) and combine it with a
winner-take-all sparsity mechanism. In addition to sparsity, we introduce
lateral recurrent connections within each layer to further protect previously
learned representations. We evaluate the new sparse-recurrent version of DFC on
the split-MNIST computer vision benchmark and show that only the combination of
sparsity and intra-layer recurrent connections improves CL performance with
respect to standard backpropagation. Our method achieves similar performance to
well-known CL methods, such as Elastic Weight Consolidation and Synaptic
Intelligence, without requiring information about task boundaries. Overall, we
showcase the idea of adopting computational principles from the brain to derive
new, task-free learning algorithms for CL.
