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Biological evidence suggests that adaptation of synaptic delays on short tomedium timescales plays an important role in learning in the brain. Inspired bybiology, we explore the feasibility and power of using synaptic delays to solvechallenging tasks even when the synaptic weights are not trained but kept atrandomly chosen fixed values. We show that training ONLY the delays infeed-forward spiking networks using backpropagation can achieve performancecomparable to the more conventional weight training. Moreover, furtherconstraining the weights to ternary values does not significantly affect thenetworks' ability to solve the tasks using only the synaptic delays. Wedemonstrate the task performance of delay-only training on MNIST andFashion-MNIST datasets in preliminary experiments. This demonstrates a newparadigm for training spiking neural networks and sets the stage for modelsthat can be more efficient than the ones that use weights for computation.

Beyond Weights: Deep learning in Spiking Neural Networks with pure synaptic-delay training