Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector. - Friedemann Zenke; Guillaume Hennequin; Wulfram Gerstner (PLoS Comput. Biol., 2013)

Limits to high-speed simulations of spiking neural networks using general-purpose computers. - Friedemann Zenke; Wulfram Gerstner (Frontiers Neuroinformatics, 2014)

Memory formation and recall in recurrent spiking neural networks. - Friedemann Zenke (2014)

Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity. - Matthieu Gilson; Cristina Savin; Friedemann Zenke (Frontiers Comput. Neurosci., 2015)

Intelligent synapses for multi-task and transfer learning. - Ben Poole; Friedemann Zenke; Surya Ganguli (ICLR (Workshop), 2017)

Continual Learning Through Synaptic Intelligence. - Friedemann Zenke; Ben Poole; Surya Ganguli (ICML, 2017)

SuperSpike: Supervised learning in multi-layer spiking neural networks. - Friedemann Zenke; Surya Ganguli (CoRR, 2017)

Improved multitask learning through synaptic intelligence. - Friedemann Zenke; Ben Poole; Surya Ganguli (CoRR, 2017)

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks. - Friedemann Zenke; Surya Ganguli (Neural Comput., 2018)

Surrogate Gradient Learning in Spiking Neural Networks. - Emre O. Neftci; Hesham Mostafa; Friedemann Zenke (CoRR, 2019)

The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks. - Benjamin Cramer; Yannik Stradmann; Johannes Schemmel; Friedemann Zenke (CoRR, 2019)

Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks. - Emre O. Neftci; Hesham Mostafa; Friedemann Zenke (IEEE Signal Process. Mag., 2019)

Finding trainable sparse networks through Neural Tangent Transfer. - Tianlin Liu; Friedemann Zenke (ICML, 2020)

A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. - Basile Confavreux; Friedemann Zenke; Everton J. Agnes; Timothy P. Lillicrap; Tim P. Vogels (NeurIPS, 2020)

Training spiking multi-layer networks with surrogate gradients on an analog neuromorphic substrate. - Benjamin Cramer; Sebastian Billaudelle; Simeon Kanya; Aron Leibfried; Andreas Grübl; Vitali Karasenko; Christian Pehle; Korbinian Schreiber; Yannik Stradmann; Johannes Weis; Johannes Schemmel; Friedemann Zenke (CoRR, 2020)

The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks. - Friedemann Zenke; Tim P. Vogels (Neural Comput., 2021)

Brain-Inspired Learning on Neuromorphic Substrates. - Friedemann Zenke; Emre O. Neftci (Proc. IEEE, 2021)

Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations. - Axel Laborieux; Friedemann Zenke (NeurIPS, 2022)

Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware. - Simon F. Müller-Cleve; Vittorio Fra; Lyes Khacef; Alejandro Pequeño-Zurro; Daniel Klepatsch; Evelina Forno; Diego Gigena Ivanovich; Shavika Rastogi; Gianvito Urgese; Friedemann Zenke; Chiara Bartolozzi (CoRR, 2022)

Predictor networks and stop-grads provide implicit variance regularization in BYOL/SimSiam. - Manu Srinath Halvagal; Axel Laborieux; Friedemann Zenke (CoRR, 2022)

Surrogate gradients for analog neuromorphic computing. - Benjamin Cramer; Sebastian Billaudelle; Simeon Kanya; Aron Leibfried; Andreas Grübl; Vitali Karasenko; Christian Pehle; Korbinian Schreiber; Yannik Stradmann; Johannes Weis; Johannes Schemmel; Friedemann Zenke (Proc. Natl. Acad. Sci. USA, 2022)

The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks. - Benjamin Cramer; Yannik Stradmann; Johannes Schemmel; Friedemann Zenke (IEEE Trans. Neural Networks Learn. Syst., 2022)

Heidelberg Spiking Datasets. - Benjamin Cramer; Yannik Stradmann; Johannes Schemmel; Friedemann Zenke (IEEE DataPort, 2022)

Fluctuation-driven initialization for spiking neural network training. - Julian Rossbroich; Julia Gygax; Friedemann Zenke (Neuromorph. Comput. Eng., 2022)

PetaOps/W edge-AI $ mu$ Processors: Myth or reality? - Manil Dev Gomony; Floran de Putter; Anteneh Gebregiorgis; Gianna Paulin; Linyan Mei; Vikram Jain; Said Hamdioui; Victor Sanchez; Tobias Grosser; Marc Geilen; Marian Verhelst; Friedemann Zenke; et al. (DATE, 2023)

Implicit variance regularization in non-contrastive SSL. - Manu Srinath Halvagal; Axel Laborieux; Friedemann Zenke (NeurIPS, 2023)

Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity. - Julian Rossbroich; Friedemann Zenke (NeurIPS, 2023)

Editorial: Focus issue on machine learning for neuromorphic engineering. - Melika Payvand; Emre Neftci; Friedemann Zenke (Neuromorph. Comput. Eng., 2023)

Decoding Finger Velocity from Cortical Spike Trains with Recurrent Spiking Neural Networks. - Tengjun Liu; Julia Gygax; Julian Rossbroich; Yansong Chua; Shaomin Zhang; Friedemann Zenke (BioCAS, 2024)

Invited: Achieving PetaOps/W Edge-AI Processing. - Manil Dev Gomony; Bas Ahn; Rick Luiken; Yashvardhan Biyani; Anteneh Gebregiorgis; Axel Laborieux; Friedemann Zenke; Said Hamdioui; Henk Corporaal (DAC, 2024)

Improving equilibrium propagation without weight symmetry through Jacobian homeostasis. - Axel Laborieux; Friedemann Zenke (ICLR, 2024)

Resource-Efficient Speech Quality Prediction through Quantization Aware Training and Binary Activation Maps. - Mattias Nilsson; Riccardo Miccini; Clement Laroche; Tobias Piechowiak; Friedemann Zenke (INTERSPEECH, 2024)

Elucidating the theoretical underpinnings of surrogate gradient learning in spiking neural networks. - Julia Gygax; Friedemann Zenke (CoRR, 2024)

Theories of synaptic memory consolidation and intelligent plasticity for continual learning. - Friedemann Zenke; Axel Laborieux (CoRR, 2024)