<ul><li>Spiking neural networks (SNNs) rely on the timing of signals for representing and processing information.</li><li>A new method called DelGrad has been proposed to compute exact loss gradients for both synaptic weights and delays in SNNs.</li><li>DelGrad eliminates the need for tracking additional variables and offers higher precision, efficiency, and suitability for neuromorphic hardware.</li><li>Experimental results demonstrate the memory efficiency, accuracy benefits, and potential for stabilizing networks against noise in SNNs trained using DelGrad on a neuromorphic platform.</li></ul>

DelGrad: Exact event-based gradients in spiking networks for training delays and weights

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