Exact Gradients for Learning Transmission Delays and Weights in Spiking Neural Networks

This work presents DelGrad, an analytical approach for calculating exact loss gradients with respect to both synaptic weights and transmission delays in an event-based spiking neural network. The inclusion of delays enriches the model's search space with a temporal dimension, improving accuracy and parameter efficiency.