  The ability to learn continuously from an incoming data stream without
catastrophic forgetting is critical for designing intelligent systems. Many
existing approaches to continual learning rely on stochastic gradient descent
and its variants. However, these algorithms have to implement various
strategies, such as memory buffers or replay, to overcome well-known
shortcomings of stochastic gradient descent methods in terms of stability,
greed, and short-term memory.
  To that end, we develop a biologically-inspired light weight neural network
architecture that incorporates local learning and neuromodulation to enable
input processing over data streams and online learning. Next, we address the
challenge of hyperparameter selection for tasks that are not known in advance
by implementing transfer metalearning: using a Bayesian optimization to explore
a design space spanning multiple local learning rules and their
hyperparameters, we identify high performing configurations in classical single
task online learning and we transfer them to continual learning tasks with
task-similarity considerations.
  We demonstrate the efficacy of our approach on both single task and continual
learning setting. For the single task learning setting, we demonstrate superior
performance over other local learning approaches on the MNIST, Fashion MNIST,
and CIFAR-10 datasets. Using high performing configurations metalearned in the
single task learning setting, we achieve superior continual learning
performance on Split-MNIST, and Split-CIFAR-10 data as compared with other
memory-constrained learning approaches, and match that of the state-of-the-art
memory-intensive replay-based approaches.
