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ORCIDFriedemann Zenke
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2020 – today
- 2024
[c9]Axel Laborieux, Friedemann Zenke:
Improving equilibrium propagation without weight symmetry through Jacobian homeostasis. ICLR 2024- [i17]Julia Gygax, Friedemann Zenke:
Elucidating the theoretical underpinnings of surrogate gradient learning in spiking neural networks. CoRR abs/2404.14964 (2024) - [i16]Friedemann Zenke, Axel Laborieux:
Theories of synaptic memory consolidation and intelligent plasticity for continual learning. CoRR abs/2405.16922 (2024) - [i15]Mattias Nilsson, Riccardo Miccini
, Clément Laroche, Tobias Piechowiak, Friedemann Zenke:
Resource-Efficient Speech Quality Prediction through Quantization Aware Training and Binary Activation Maps. CoRR abs/2407.04578 (2024) - [i14]Tengjun Liu, Julia Gygax, Julian Rossbroich, Yansong Chua, Shaomin Zhang, Friedemann Zenke:
Decoding finger velocity from cortical spike trains with recurrent spiking neural networks. CoRR abs/2409.01762 (2024) - 2023
- [j11]Melika Payvand, Emre Neftci, Friedemann Zenke:
Editorial: Focus issue on machine learning for neuromorphic engineering. Neuromorph. Comput. Eng. 3(3): 30403 (2023) - [c8]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, Frank K. Gürkaynak, Barry de Bruin, Sander Stuijk, Simon Davidson, Sayandip De, Mounir Ghogho, Alexandra Jimborean, Sherif Eissa, Luca Benini, Dimitrios Soudris, Rajendra Bishnoi, Sam Ainsworth, Federico Corradi, Ouassim Karrakchou, Tim Güneysu, Henk Corporaal:
PetaOps/W edge-AI $\mu$ Processors: Myth or reality? DATE 2023: 1-6 - [c7]Manu Srinath Halvagal, Axel Laborieux, Friedemann Zenke:
Implicit variance regularization in non-contrastive SSL. NeurIPS 2023 - [c6]Julian Rossbroich, Friedemann Zenke:
Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity. NeurIPS 2023 - [i13]Axel Laborieux, Friedemann Zenke:
Improving equilibrium propagation without weight symmetry through Jacobian homeostasis. CoRR abs/2309.02214 (2023) - [i12]Julian Rossbroich, Friedemann Zenke:
Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity. CoRR abs/2310.19614 (2023) - 2022
- [j10]Julian Rossbroich, Julia Gygax, Friedemann Zenke:
Fluctuation-driven initialization for spiking neural network training. Neuromorph. Comput. Eng. 2(4): 44016 (2022) - [j9]Benjamin Cramer, Sebastian Billaudelle, Simeon Kanya, Aron Leibfried, Andreas Grübl, Vitali Karasenko, Christian Pehle, Korbinian Schreiber, Yannik Stradmann, Johannes Weis, Johannes Schemmel, Friedemann Zenke:
Surrogate gradients for analog neuromorphic computing. Proc. Natl. Acad. Sci. USA 119(4): e2109194119 (2022) - [j8]Benjamin Cramer, Yannik Stradmann, Johannes Schemmel, Friedemann Zenke:
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 33(7): 2744-2757 (2022) - [c5]Axel Laborieux, Friedemann Zenke:
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations. NeurIPS 2022 - [d1]Benjamin Cramer, Yannik Stradmann, Johannes Schemmel, Friedemann Zenke:
Heidelberg Spiking Datasets. IEEE DataPort, 2022
- [i11]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:
Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware. CoRR abs/2205.15864 (2022) - [i10]Julian Rossbroich, Julia Gygax, Friedemann Zenke:
Fluctuation-driven initialization for spiking neural network training. CoRR abs/2206.10226 (2022) - [i9]Axel Laborieux, Friedemann Zenke:
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations. CoRR abs/2209.00530 (2022) - [i8]Manu Srinath Halvagal, Axel Laborieux, Friedemann Zenke:
Predictor networks and stop-grads provide implicit variance regularization in BYOL/SimSiam. CoRR abs/2212.04858 (2022) - 2021
- [j7]Friedemann Zenke, Tim P. Vogels:
The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks. Neural Comput. 33(4): 899-925 (2021) - [j6]Friedemann Zenke, Emre O. Neftci:
Brain-Inspired Learning on Neuromorphic Substrates. Proc. IEEE 109(5): 935-950 (2021) - 2020
- [c4]Tianlin Liu, Friedemann Zenke:
Finding trainable sparse networks through Neural Tangent Transfer. ICML 2020: 6336-6347 - [c3]Basile Confavreux, Friedemann Zenke, Everton J. Agnes, Timothy P. Lillicrap, Tim P. Vogels:
A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network. NeurIPS 2020 - [i7]Benjamin Cramer, Sebastian Billaudelle, Simeon Kanya, Aron Leibfried, Andreas Grübl, Vitali Karasenko, Christian Pehle, Korbinian Schreiber, Yannik Stradmann, Johannes Weis, Johannes Schemmel, Friedemann Zenke:
Training spiking multi-layer networks with surrogate gradients on an analog neuromorphic substrate. CoRR abs/2006.07239 (2020) - [i6]Tianlin Liu, Friedemann Zenke:
Finding trainable sparse networks through Neural Tangent Transfer. CoRR abs/2006.08228 (2020) - [i5]Friedemann Zenke, Emre O. Neftci:
Brain-Inspired Learning on Neuromorphic Substrates. CoRR abs/2010.11931 (2020)
2010 – 2019
- 2019
- [j5]Emre O. Neftci, Hesham Mostafa, Friedemann Zenke:
Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks. IEEE Signal Process. Mag. 36(6): 51-63 (2019) - [i4]Emre O. Neftci, Hesham Mostafa, Friedemann Zenke:
Surrogate Gradient Learning in Spiking Neural Networks. CoRR abs/1901.09948 (2019) - [i3]Benjamin Cramer, Yannik Stradmann, Johannes Schemmel, Friedemann Zenke:
The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks. CoRR abs/1910.07407 (2019) - 2018
- [j4]Friedemann Zenke, Surya Ganguli:
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks. Neural Comput. 30(6) (2018) - 2017
- [c2]Ben Poole, Friedemann Zenke, Surya Ganguli:
Intelligent synapses for multi-task and transfer learning. ICLR (Workshop) 2017 - [c1]Friedemann Zenke, Ben Poole, Surya Ganguli:
Continual Learning Through Synaptic Intelligence. ICML 2017: 3987-3995 - [i2]Friedemann Zenke, Ben Poole, Surya Ganguli:
Improved multitask learning through synaptic intelligence. CoRR abs/1703.04200 (2017) - [i1]Friedemann Zenke, Surya Ganguli:
SuperSpike: Supervised learning in multi-layer spiking neural networks. CoRR abs/1705.11146 (2017) - 2015
- [j3]Matthieu Gilson, Cristina Savin, Friedemann Zenke:
Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity. Frontiers Comput. Neurosci. 9: 145 (2015) - 2014
- [b1]Friedemann Zenke:
Memory formation and recall in recurrent spiking neural networks. EPFL, Switzerland, 2014 - [j2]Friedemann Zenke, Wulfram Gerstner:
Limits to high-speed simulations of spiking neural networks using general-purpose computers. Frontiers Neuroinformatics 8: 76 (2014) - 2013
- [j1]Friedemann Zenke, Guillaume Hennequin, Wulfram Gerstner:
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector. PLoS Comput. Biol. 9(11) (2013)
Coauthor Index
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