Julian Göltz

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Mihai A. PetroviciGroup Leader, NeuroTMA Lab, University of BernVerified email at unibe.ch
Sebastian BillaudelleVerified email at kip.uni-heidelberg.de
Johannes SchemmelHeidelberg UniversityVerified email at kip.uni-heidelberg.de
Laura KrienerPostdoctoral Researcher, Institute of Neuroinformatics, University of Zurich & ETH ZurichVerified email at unibe.ch
Oliver BreitwieserDr. rer. nat., Heidelberg UniversityVerified email at kip.uni-heidelberg.de
Akos F. KunglCarl Zeiss SMTVerified email at zeiss.com
Dominik DoldResearch Fellow, ESA ESTECVerified email at esa.int
Walter SennUniversität BernVerified email at unibe.ch
Eric MüllerRuprecht-Karls-Universität HeidelbergVerified email at kip.uni-heidelberg.de
Christian PehleNeuroAI Scholar, Cold Spring Harbor LaboratoryVerified email at cshl.edu
Yannik StradmannKirchhoff-Institute for Physics, Heidelberg UniversityVerified email at kip.uni-heidelberg.de

Julian Göltz

PhD Student, Heidelberg University and University of Bern

Verified email at kip.uni-heidelberg.de - Homepage

Computational Neuroscience Machine Learning Brain-Inspired Computing Deep Learning Theoretical Neuroscience


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Fast and energy-efficient neuromorphic deep learning with first-spike times J Göltz, L Kriener, A Baumbach, S Billaudelle, O Breitwieser, B Cramer, ... Nature Machine Intelligence 3 (9), 823-835, 2021	122	2021
Visualizing a joint future of neuroscience and neuromorphic engineering F Zenke, SM Bohté, C Clopath, IM Comşa, J Göltz, W Maass, ... Neuron 109 (4), 571-575, 2021	85	2021
Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate S Billaudelle, Y Stradmann, K Schreiber, B Cramer, A Baumbach, D Dold, ... 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2020	58	2020
Fast and deep: energy-efficient neuromorphic learning with first-spike times J Göltz, L Kriener, A Baumbach, S Billaudelle, O Breitwieser, B Cramer, ... arXiv preprint arXiv:1912.11443, 2019	53*	2019
A scalable approach to modeling on accelerated neuromorphic hardware E Müller, E Arnold, O Breitwieser, M Czierlinski, A Emmel, J Kaiser, ... Frontiers in neuroscience 16, 884128, 2022	30	2022
The yin-yang dataset L Kriener, J Göltz, MA Petrovici Proceedings of the 2022 Annual Neuro-Inspired Computational Elements …, 2022	29	2022
Training deep networks with time-to-first-spike coding on the brainscales wafer-scale system J Göltz Masterarbeit, Universität Heidelberg, April, 2019	5	2019
Fast and Energy-efficient deep Neuromorphic Learning J Göltz, L Kriener, V Sabado, MA Petrovici ERCIM News, 17, 2021	2	2021
DelGrad: Exact gradients in spiking networks for learning transmission delays and weights J Göltz, J Weber, L Kriener, P Lake, M Payvand, MA Petrovici arXiv preprint arXiv:2404.19165, 2024	1	2024
Gradient-based methods for spiking physical systems J Göltz, S Billaudelle, L Kriener, L Blessing, C Pehle, E Müller, ... arXiv preprint arXiv:2309.10823, 2023	1	2023
Lu. i--A low-cost electronic neuron for education and outreach Y Stradmann, J Göltz, MA Petrovici, J Schemmel, S Billaudelle arXiv preprint arXiv:2404.16664, 2024		2024

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