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Computational Neuroscience at the FMI

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September 1, 2020January 19, 2021 fzenkejobs

Hiring: Information processing in spiking neural networks

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

August 6, 2020September 2, 2020 fzenkeconferences, News

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

June 30, 2020 fzenkefmi, publications

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

June 30, 2020January 22, 2022 fzenkepreprint

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

June 16, 2020June 17, 2020 fzenkepublications

Paper: Finding sparse trainable neural networks through Neural Tangent Transfer

New paper led by Tianlin Liu on “Finding sparse trainable neural networks through Neural Tangent Transfer” https://arxiv.org/abs/2006.08228  (and code) which was accepted at ICML. In the paper we leverage the neural tangent kernel to instantiate sparse neuralContinue reading

June 12, 2020March 5, 2022 fzenkepublications

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

February 5, 2020June 16, 2020 fzenketalk

Talk: Bench to Bedside Symposium at Uni Basel

On Friday 7th February 2020, 09:00, Friedemann will talk about “Building functional neural networks in-silico” at the Bench to Bedside Symposium at Uni Basel Pharmazentrum, Hörsaal 1, Klingelbergstrasse 50/70, 4056 Basel.

November 8, 2019February 25, 2020 fzenkepublications

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

October 29, 2019October 29, 2019 fzenkepublications

Perspective: A deep learning framework for neuroscience

Our perspective paper on how systems neuroscience can benefit from deep learning was published today. In work led by Blake Richards, Tim Lillicrap, and Konrad Kording, we argue that focusing on the three core elements usedContinue reading

October 28, 2019December 31, 2020 fzenkepublications

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

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