  Backpropagation is widely used to train artificial neural networks, but its
relationship to synaptic plasticity in the brain is unknown. Some biological
models of backpropagation rely on feedback projections that are symmetric with
feedforward connections, but experiments do not corroborate the existence of
such symmetric backward connectivity. Random feedback alignment offers an
alternative model in which errors are propagated backward through fixed, random
backward connections. This approach successfully trains shallow models, but
learns slowly and does not perform well with deeper models or online learning.
In this study, we develop a meta-learning approach to discover interpretable,
biologically plausible plasticity rules that improve online learning
performance with fixed random feedback connections. The resulting plasticity
rules show improved online training of deep models in the low data regime. Our
results highlight the potential of meta-learning to discover effective,
interpretable learning rules satisfying biological constraints.
