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Friedemann Zenke

Showing results (1-10 of 25) with videos related to

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Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences|January 18, 2017
Hebbian plasticity requires compensatory processes on multiple timescalesFriedemann Zenke, Wulfram Gerstner
Frontiers in Neuroinformatics|October 14, 2014
Limits to high-speed simulations of spiking neural networks using general-purpose computersFriedemann Zenke, Wulfram Gerstner
Neural Computation|April 14, 2018
SuperSpike: Supervised Learning in Multilayer Spiking Neural NetworksFriedemann Zenke, Surya Ganguli
Neural Computation|March 20, 2025
Elucidating the Theoretical Underpinnings of Surrogate Gradient Learning in Spiking Neural NetworksJulia Gygax, Friedemann Zenke
Elife|December 13, 2021
Nonlinear transient amplification in recurrent neural networks with short-term plasticityYue Kris Wu, Friedemann Zenke
Nature Neuroscience|October 12, 2023
The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networksManu Srinath Halvagal, Friedemann Zenke
Neural Computation|January 29, 2021
The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural NetworksFriedemann Zenke, Tim P Vogels
Proceedings of Machine Learning Research|January 8, 2020
Continual Learning Through Synaptic IntelligenceFriedemann Zenke, Ben Poole, Surya Ganguli
Plos Computational Biology|November 19, 2013
Synaptic plasticity in neural networks needs homeostasis with a fast rate detectorFriedemann Zenke, Guillaume Hennequin, Wulfram Gerstner
Frontiers in Computational Neuroscience|December 10, 2015
Editorial: Emergent Neural Computation from the Interaction of Different Forms of PlasticityMatthieu Gilson, Cristina Savin, Friedemann Zenke
Pageof 3

Showing results (1-10 of 25) with videos related to

Sort By:
Pageof 3
Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences|January 18, 2017
Hebbian plasticity requires compensatory processes on multiple timescalesFriedemann Zenke, Wulfram Gerstner
Frontiers in Neuroinformatics|October 14, 2014
Limits to high-speed simulations of spiking neural networks using general-purpose computersFriedemann Zenke, Wulfram Gerstner
Neural Computation|April 14, 2018
SuperSpike: Supervised Learning in Multilayer Spiking Neural NetworksFriedemann Zenke, Surya Ganguli
Neural Computation|March 20, 2025
Elucidating the Theoretical Underpinnings of Surrogate Gradient Learning in Spiking Neural NetworksJulia Gygax, Friedemann Zenke
Elife|December 13, 2021
Nonlinear transient amplification in recurrent neural networks with short-term plasticityYue Kris Wu, Friedemann Zenke
Nature Neuroscience|October 12, 2023
The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networksManu Srinath Halvagal, Friedemann Zenke
Neural Computation|January 29, 2021
The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural NetworksFriedemann Zenke, Tim P Vogels
Proceedings of Machine Learning Research|January 8, 2020
Continual Learning Through Synaptic IntelligenceFriedemann Zenke, Ben Poole, Surya Ganguli
Plos Computational Biology|November 19, 2013
Synaptic plasticity in neural networks needs homeostasis with a fast rate detectorFriedemann Zenke, Guillaume Hennequin, Wulfram Gerstner
Frontiers in Computational Neuroscience|December 10, 2015
Editorial: Emergent Neural Computation from the Interaction of Different Forms of PlasticityMatthieu Gilson, Cristina Savin, Friedemann Zenke
Pageof 3