Julian Göltz

According to our database, Julian Göltz authored at least 13 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Spiking Matters: Exact Gradients for Deep Spiking Networks and Neuromorphic Hardware.

Julian Göltz

PhD thesis, 2026

2025

Synchronization and semantization in deep spiking networks.

Jonas Oberste-Frielinghaus, Anno C. Kurth, Julian Göltz, Laura Kriener, Junji Ito, Mihai A. Petrovici, Sonja Grün

CoRR, August, 2025

2024

DelGrad: Exact gradients in spiking networks for learning transmission delays and weights.

Julian Göltz, Jimmy Weber, Laura Kriener, Peter Lake, Melika Payvand, Mihai A. Petrovici

CoRR, 2024

Lu.i - A low-cost electronic neuron for education and outreach.

Yannik Stradmann, Julian Göltz, Mihai A. Petrovici, Johannes Schemmel, Sebastian Billaudelle

CoRR, 2024

2023

Gradient-based methods for spiking physical systems.

Julian Göltz, Sebastian Billaudelle, Laura Kriener, Luca Blessing, Christian Pehle, Eric Müller, Johannes Schemmel, Mihai A. Petrovici

CoRR, 2023

2022

A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware.

Eric Müller, Elias Arnold, Oliver Breitwieser, Milena Czierlinski, Arne Emmel, Jakob Kaiser, Christian Mauch, Sebastian Schmitt, Philipp Spilger, Raphael Stock, Yannik Stradmann, Johannes Weis, Andreas Baumbach, Sebastian Billaudelle, Benjamin Cramer, Falk Leonard Ebert, Julian Göltz, Joscha Ilmberger, Vitali Karasenko, Mitja Kleider, Aron Leibfried, Christian Pehle, Johannes Schemmel

CoRR, 2022

The Yin-Yang dataset.

Laura Kriener, Julian Göltz, Mihai A. Petrovici

Proceedings of the NICE 2022: Neuro-Inspired Computational Elements Conference, 2022

2021

Fast and energy-efficient neuromorphic deep learning with first-spike times.

Julian Göltz, Laura Kriener, Andreas Baumbach, Sebastian Billaudelle, Oliver Breitwieser, Benjamin Cramer, Dominik Dold, Ákos F. Kungl, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

Nat. Mach. Intell., 2021

Fast and Energy-efficient Deep Neuromorphic Learning.

Julian Göltz, Laura Kriener, Virginie Sabado, Mihai A. Petrovici

ERCIM News, 2021

2020

Fast and deep neuromorphic learning with first-spike coding.

Julian Göltz, Andreas Baumbach, Sebastian Billaudelle, Ákos F. Kungl, Oliver Breitwieser, Karlheinz Meier, Johannes Schemmel, Laura Kriener, Mihai A. Petrovici

Proceedings of the NICE '20: Neuro-inspired Computational Elements Workshop, 2020

Live Demonstration: Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate.

Sebastian Billaudelle, Yannik Stradmann, Korbinian Schreiber, Benjamin Cramer, Andreas Baumbach, Dominik Dold, Julian Göltz, Ákos Ferenc Kungl, Timo C. Wunderlich, Andreas Hartel, Eric Müller, Oliver Breitwieser, Christian Mauch, Mitja Kleider, Andreas Grübl, David Stöckel, Christian Pehle, Arthur Heimbrecht, Philipp Spilger, Gerd Kiene, Vitali Karasenko, Walter Senn, Mihai A. Petrovici, Johannes Schemmel, Karlheinz Meier

Proceedings of the IEEE International Symposium on Circuits and Systems, 2020

Versatile Emulation of Spiking Neural Networks on an Accelerated Neuromorphic Substrate.

Sebastian Billaudelle, Yannik Stradmann, Korbinian Schreiber, Benjamin Cramer, Andreas Baumbach, Dominik Dold, Julian Göltz, Ákos Ferenc Kungl, Timo C. Wunderlich, Andreas Hartel, Eric Müller, Oliver Breitwieser, Christian Mauch, Mitja Kleider, Andreas Grübl, David Stöckel, Christian Pehle, Arthur Heimbrecht, Philipp Spilger, Gerd Kiene, Vitali Karasenko, Walter Senn, Mihai A. Petrovici, Johannes Schemmel, Karlheinz Meier

Proceedings of the IEEE International Symposium on Circuits and Systems, 2020

2019

Fast and deep neuromorphic learning with time-to-first-spike coding.

Julian Göltz, Andreas Baumbach, Sebastian Billaudelle, Oliver Breitwieser, Dominik Dold, Laura Kriener, Ákos Ferenc Kungl, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

CoRR, 2019