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Friedemann Zenke
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2020 – today
- 2024
- [c10]Manil Dev Gomony
, Bas Ahn
Invited: Achieving PetaOps/W Edge-AI Processing. DAC 2024: 370:1-370:4 - [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
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
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
Fluctuation-driven initialization for spiking neural network training. Neuromorph. Comput. Eng. 2(4): 44016 (2022) - [j9]Benjamin Cramer
Surrogate gradients for analog neuromorphic computing. Proc. Natl. Acad. Sci. USA 119(4): e2109194119 (2022) - [j8]Benjamin Cramer
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
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
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
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
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
The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks. CoRR abs/1910.07407 (2019) - 2018
- [j4]Friedemann Zenke
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
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
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
Limits to high-speed simulations of spiking neural networks using general-purpose computers. Frontiers Neuroinformatics 8: 76 (2014) - 2013
- [j1]Friedemann Zenke
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector. PLoS Comput. Biol. 9(11) (2013)
Coauthor Index
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