  Existing Continual Learning (CL) approaches have focused on addressing
catastrophic forgetting by leveraging regularization methods, replay buffers,
and task-specific components. However, realistic CL solutions must be shaped
not only by metrics of catastrophic forgetting but also by computational
efficiency and running time. Here, we introduce a novel neural network
architecture inspired by neuromodulation in biological nervous systems to
economically and efficiently address catastrophic forgetting and provide new
avenues for interpreting learned representations. Neuromodulation is a
biological mechanism that has received limited attention in machine learning;
it dynamically controls and fine tunes synaptic dynamics in real time to track
the demands of different behavioral contexts. Inspired by this, our proposed
architecture learns a relatively small set of parameters per task context that
\emph{neuromodulates} the activity of unchanging, randomized weights that
transform the input. We show that this approach has strong learning performance
per task despite the very small number of learnable parameters. Furthermore,
because context vectors are so compact, multiple networks can be stored
concurrently with no interference and little spatial footprint, thus completely
eliminating catastrophic forgetting and accelerating the training process.
