We just put up a new preprint https://www.biorxiv.org/content/10.1101/2020.06.29.176925v1 in which we take a careful look at what makes surrogate gradients work. Spiking neural networks are notoriously hard to train using gradient-based methods due to their binary spiking nonlinearity. To deal …

Preprint: The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks Read more »

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Thrilled to share the latest results on learning in multi-layer spiking networks using biologically plausible surrogate gradients at the “Third workshop on advanced methods in theoretical neuroscience” at the Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany. Thanks to …

Talk at MPI Göttingen on June 28th Read more »

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I am happy to announce that the SuperSpike paper and code are finally published. Here is an example of a network with one hidden layer which is learning to produce a Radcliffe Camera spike train from frozen Poisson input spike …

SuperSpike: Supervised learning in spiking neural networks — paper and code published Read more »

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