We are looking for Ph.D. students to work on the computational principles of information processing in spiking neural networks. The project strives to understand computation in the sparse spiking and sparse connectivity regime, in whichContinue reading
Tag: spiking neural networks
Online workshop: Spiking neural networks as universal function approximators
Dan Goodman and myself are organizing an online workshop on new approaches to training spiking neural networks, Aug 31st / Sep 1st 2020. Invited speakers: Sander Bohte (CWI), Iulia M. Comsa (Google), Franz Scherr (TUG), Emre Neftci (UC Irvine),Continue reading
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
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
Paper: Surrogate gradients for analog neuromorphic computing
Update (22.01.2022): Now published as Cramer, B., Billaudelle, S., Kanya, S., Leibfried, A., Grübl, A., Karasenko, V., Pehle, C., Schreiber, K., Stradmann, Y., Weis, J., et al. (2022). Surrogate gradients for analog neuromorphic computing. PNASContinue reading
Paper: Surrogate Gradient Learning in Spiking Neural Networks
“Bringing the Power of Gradient-based optimization to spiking neural networks” We are happy to announce that our tutorial paper on Surrogate Gradient Learning in spiking neural networks now appeared in the IEEE Signal Processing Magazine.Continue reading
Preprint: The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks
Update (2020-12-30) Now published: Cramer, B., Stradmann, Y., Schemmel, J., and Zenke, F. (2020). The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning SystemsContinue reading
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: https://github.com/fzenke/spytorch Emre, Hesham, and myself are planning toContinue 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