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 theirContinue reading
Tag: supervised learning
Preprint: The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
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 theirContinue reading
Talk at MPI Göttingen on June 28th
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 andContinue reading
SuperSpike: Supervised learning in spiking neural networks — paper and code published
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 trainContinue reading
Supervised learning in multi-layer spiking neural networks
We just put a conference paper version of “SuperSpike”, our work on supervised learning in multi-layer spiking neural networks to the arXiv https://arxiv.org/abs/1705.11146. As always I am keen to get your feedback.