  Online learning holds the promise of enabling efficient long-term credit
assignment in recurrent neural networks. However, current algorithms fall short
of offline backpropagation by either not being scalable or failing to learn
long-range dependencies. Here we present a high-performance online learning
algorithm that merely doubles the memory and computational requirements of a
single inference pass. We achieve this by leveraging independent recurrent
modules in multi-layer networks, an architectural motif that has recently been
shown to be particularly powerful. Experiments on synthetic memory problems and
on the challenging long-range arena benchmark suite reveal that our algorithm
performs competitively, establishing a new standard for what can be achieved
through online learning. This ability to learn long-range dependencies offers a
new perspective on learning in the brain and opens a promising avenue in
neuromorphic computing.
