ePoster · Computational Neuroscience
DelGrad: Exact gradients in spiking networks for learning transmission delays and weights
Julian Göltz, Jimmy Weber, Laura Kriener, Peter Lake, Melika Payvand, Mihai Petrovici · Bernstein Conference 2024
Spiking neural networks (SNNs) inherently rely on the timing of signals for representing and processing information. Transmission delays play an important role in shaping these temporal characteristics. Recent work [1, 2] has demonstrated the substantial advantages of learning these delays along with synaptic weights, both in terms of accuracy and memory efficiency. However, these approaches suffer from drawbacks in terms of precision and learning efficiency, as they operate in discrete time and with approximate gradients, while also requiring membrane potential recordings for calculating para