Happy to share this report summarizing the key contributions, discussion outcomes, and future research directions as presented at the spiking neural networks as universal function approximators meeting SNUFA2020. Paper: https://www.cell.com/neuron/fulltext/S0896-6273(21)00009-XAuthor link: https://authors.elsevier.com/a/1cbg3_KOmxI%7E6f
Category: publications
Paper: Brain-Inspired Learning on Neuromorphic Substrates
I’m happy to share our new overview paper (https://ieeexplore.ieee.org/document/9317744, preprint: arxiv.org/abs/2010.11931) on brain-inspired learning on neuromorphic substrates in (spiking) recurrent neural networks. We systematically analyze how the combination of Real-Time-Recurrent Learning (RTRL; Williams and Zipser,Continue reading
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
Preprint: Training spiking multi-layer networks with surrogate gradients on an analog neuromorphic substrate
We are happy to share our new preprint “Training spiking multi-layer networks with surrogate gradients on an analog neuromorphic substrate” (https://arxiv.org/abs/2006.07239). This work led by Benjamin Cramer and Sebastian Billaudelle and is a joint effortContinue 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
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
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