  Existing machines are functionally specific tools that were made for easy
prediction and control. Tomorrow's machines may be closer to biological systems
in their mutability, resilience, and autonomy. But first they must be capable
of learning and retaining new information without being exposed to it
arbitrarily often. Past efforts to engineer such systems have sought to build
or regulate artificial neural networks using disjoint sets of weights that are
uniquely sensitive to specific tasks or inputs. This has not yet enabled
continual learning over long sequences of previously unseen data without
corrupting existing knowledge: a problem known as catastrophic forgetting. In
this paper, we introduce a system that can learn sequentially over previously
unseen datasets (ImageNet, CIFAR-100) with little forgetting over time. This is
done by controlling the activity of weights in a convolutional neural network
on the basis of inputs using top-down regulation generated by a second
feed-forward neural network. We find that our method learns continually under
domain transfer with sparse bursts of activity in weights that are recycled
across tasks, rather than by maintaining task-specific modules. Sparse synaptic
bursting is found to balance activity and suppression such that new functions
can be learned without corrupting extant knowledge, thus mirroring the balance
of order and disorder in systems at the edge of chaos. This behavior emerges
during a prior pre-training (or 'meta-learning') phase in which regulated
synapses are selectively disinhibited, or grown, from an initial state of
uniform suppression through prediction error minimization.
