Research

Selected Publications

Event-based backpropagation can compute exact gradients for spiking neural networks.
By T. C. Wunderlich, C. Pehle.
In Scientific Reports vol. 11, 2021.

Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate.
By S. Billaudelle, Y. Stradmann, K. Schreiber, B. Cramer, A. Baumbach, D. Dold, J. Göltz, A. F. Kungl, T. C. Wunderlich, A. Hartel, E. Müller, O. Breitwieser, C. Mauch, M. Kleider, A. Grübl, D. Stöckel, C. Pehle, A. Heimbrecht, P. Spilger, G. Kiene, V. Karasenko, W. Senn, M. A. Petrovici, J. Schemmel, K. Meier.
In 2020 IEEE International Symposium on Circuits and Systems, 2020.

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study.
By T. C. Wunderlich, A. F. Kungl, E. Müller, A. Hartel, Y. Stradmann, S. A. Aamir, A. Grübl, A. Heimbrecht, K. Schreiber, D. Stöckel, C. Pehle, S. Billaudelle, G. Kiene, C. Mauch, J. Schemmel, K. Meier, M. A. Petrovici.
In Frontiers in Neuroscience vol. 13, 2019.

Closed-loop experiments on the BrainScaleS-2 architecture.
By K. Schreiber, T. C. Wunderlich, C. Pehle, M. A. Petrovici, J. Schemmel, K. Meier.
In Proceedings of the Neuro-inspired Computational Elements Workshop, 2020.

Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network.
By T. C. Wunderlich, A. F. Kungl, E. Müller, J. Schemmel, M. Petrovici.
In Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation, 2019.