  Interest in biologically inspired alternatives to backpropagation is driven
by the desire to both advance connections between deep learning and
neuroscience and address backpropagation's shortcomings on tasks such as
online, continual learning. However, local synaptic learning rules like those
employed by the brain have so far failed to match the performance of
backpropagation in deep networks. In this study, we employ meta-learning to
discover networks that learn using feedback connections and local, biologically
inspired learning rules. Importantly, the feedback connections are not tied to
the feedforward weights, avoiding biologically implausible weight transport.
Our experiments show that meta-trained networks effectively use feedback
connections to perform online credit assignment in multi-layer architectures.
Surprisingly, this approach matches or exceeds a state-of-the-art
gradient-based online meta-learning algorithm on regression and classification
tasks, excelling in particular at continual learning. Analysis of the weight
updates employed by these models reveals that they differ qualitatively from
gradient descent in a way that reduces interference between updates. Our
results suggest the existence of a class of biologically plausible learning
mechanisms that not only match gradient descent-based learning, but also
overcome its limitations.
