We also classified the Spiking Speech Commands (SSC) with good success. Finally, the GeNN implementation of Eventprop has very beneficial computational scaling properties compared to BPTT in Spyx (github.com/kmheckel/spyx). All details at doi.org/10.1088/2634.... @sussexai.bsky.social 5/5

We extended Eventprop to a wider class of loss functions and found that, with a ‘loss-shaping’ term, we could achieve fast and reliable learning. Combining this with 3 data augmentations, we obtained a SOTA SHD classification accuracy of 93.5±0.7% (n=8) on the test set after rigorous validation. 4/5

This initially failed due to average cross-entropy loss creating unhelpful gradients in the hidden layer and the fact that spike creation and deletion is not “visible” in the exact gradients calculated by Eventprop. 3/5

We implemented Eventprop (Wunderlich & Pehle, 2021) in our GeNN (genn-team.github.io) simulator and attempted to classify the Spiking Heidelberg Digits (SHD, zenkelab.org/resources/sp...) in a 3-layer network. 2/5

Hi Manu,
I am a Professor of Informatics and work in Computational Neuroscience and neuromorphic computing. I would like to contribute to the Science feed. My staff page is profiles.sussex.ac.uk/p206151-thom... and Google Scholar scholar.google.com/citations?us...