Tutorial on surrogate gradient learning in spiking networks online

Please try this at home!

I just put up a beta version of a tutorial showing how to train spiking neural networks with surrogate gradients using PyTorch:

Emre, Hesham, and myself are planning to release a more comprehensive collection of code in the near future to accompany our tutorial paper. Stay tuned!

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2 comments on “Tutorial on surrogate gradient learning in spiking networks online
  1. Deckard says:

    Is this implementation the same as that on https://github.com/fzenke/pub2018superspike ?
    Is it known if it’s faster/slower than the original implementation?

    • fzenke says:

      This tutorial implementation is quite different from SuperSpike. Most importantly, it uses a different cost-function and back-end library. This tutorial version illustrates how to use surrogate gradients in modern ML auto-diff frameworks. Whereas SuperSpike is a fully online algorithm running on top of an event-based spiking neural network library. Since SuperSpike is normally used with a van Rossum distance loss to predefined target spike trains the present tutorial uses a crossentropy loss to do classification. Therefore both have never been compared directly. I hope that clarifies it.

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