  Biological evidence suggests that adaptation of synaptic delays on short to
medium timescales plays an important role in learning in the brain. Inspired by
biology, we explore the feasibility and power of using synaptic delays to solve
challenging tasks even when the synaptic weights are not trained but kept at
randomly chosen fixed values. We show that training ONLY the delays in
feed-forward spiking networks using backpropagation can achieve performance
comparable to the more conventional weight training. Moreover, further
constraining the weights to ternary values does not significantly affect the
networks' ability to solve the tasks using only the synaptic delays. We
demonstrate the task performance of delay-only training on MNIST and
Fashion-MNIST datasets in preliminary experiments. This demonstrates a new
paradigm for training spiking neural networks and sets the stage for models
that can be more efficient than the ones that use weights for computation.
