Friedemann Zenke

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Halvagal MS, Zenke F. (2023) The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks. Nature Neuroscience. 26: 1906-1915
Cramer B, Billaudelle S, Kanya S, et al. (2022) Surrogate gradients for analog neuromorphic computing. Proceedings of the National Academy of Sciences of the United States of America. 119
Wu YK, Zenke F. (2021) Nonlinear transient amplification in recurrent neural networks with short-term plasticity. Elife. 10
Payeur A, Guerguiev J, Zenke F, et al. (2021) Author Correction: Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. Nature Neuroscience. 24: 1780
Payeur A, Guerguiev J, Zenke F, et al. (2021) Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits. Nature Neuroscience
Zenke F, Bohté SM, Clopath C, et al. (2021) Visualizing a joint future of neuroscience and neuromorphic engineering. Neuron. 109: 571-575
Zenke F, Vogels TP. (2021) The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks. Neural Computation. 1-27
Cramer B, Stradmann Y, Schemmel J, et al. (2020) The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks. Ieee Transactions On Neural Networks and Learning Systems
Richards BA, Lillicrap TP, Beaudoin P, et al. (2019) A deep learning framework for neuroscience. Nature Neuroscience. 22: 1761-1770
Neftci EO, Mostafa H, Zenke F. (2019) Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks Ieee Signal Processing Magazine. 36: 51-63