Overview on Google scholar
- Wu, Y.K., and Zenke, F. (preprint).
Nonlinear transient amplification in recurrent neural networks with short-term plasticity.
preprint | code - Cramer, B., Billaudelle, S., Kanya, S., Leibfried, A., Grübl, A., Karasenko, V., Pehle, C., Schreiber, K., Stradmann, Y., Weis, J., Schemmel, J., Zenke, F. (submitted).
Surrogate gradients for analog neuromorphic computing.
preprint - Payeur, A., Guerguiev, J., Zenke, F., Richards, B.A., and Naud, R. (2021).
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits.
Nature Neuroscience 1–10.
fulltext | author link | preprint - Zenke, F., Bohté, S.M., Clopath, C., Comşa, I.M., Göltz, J., Maass, W., Masquelier, T., Naud, R., Neftci, E.O., Petrovici, M.A., et al. (2021).
Visualizing a joint future of neuroscience and neuromorphic engineering.
Neuron 109, 571–575.
fulltext | unpaywalled - Zenke, F., and Vogels, T.P. (2021).
The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks.
Neural Computation 1–27.
fulltext | preprint | errata - Zenke, F., and Neftci, E.O. (2021).
Brain-Inspired Learning on Neuromorphic Substrates.
Proceedings of the IEEE 1–16.
fulltext | preprint - Cramer, B., Stradmann, Y., Schemmel, J., and Zenke, F. (2020).
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks.
IEEE Transactions on Neural Networks and Learning Systems 1–14.
fulltext | preprint | data | code - Confavreux, B., Zenke, F., Agnes, E., Lillicrap, T., and Vogels, T. (2020). A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network.
NeurIPS 33.
fulltext | preprint - Liu, T., and Zenke, F. (2020).
Finding trainable sparse networks through Neural Tangent Transfer.
Proceedings of the 37th International Conference on Machine Learning (ICML),.
fulltext | preprint | code - Neftci, E.O., Mostafa, H., Zenke, F. (2019).
Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks.
IEEE SPM 36, 51–63.
fulltext | preprint | code - Richards, BA., Lillicrap, TP., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R., de Berker, A., Ganguli, S., Gillon, C., Hafner, D., Kepecs, A., Kriegeskorte, N., Latham, P., Lindsay, GW., Miller, KD., Naud, R, Pack, CC., Poirazi, P., Roelfsema, P., Sacramento, J., Saxe, A., Scellier, B., Schapiro, AC., Senn, W., Wayne, G., Yamins, D., Zenke, F., Zylberberg, J., Therien, D., Kording, KP. (2019).
A deep learning framework for neuroscience.
Nature Neuroscience 22(11), 1761–1770.
fulltext - Gjoni, E., Zenke, F., Bouhours, B., and Schneggenburger R. (2018). Specific synaptic input strengths determine the computational properties of excitation-inhibition integration in a sound localization circuit. J Physiol 596, 4945–4967.
fulltext - Zenke, F. and Ganguli, S. (2018).
SuperSpike: Supervised learning in multi-layer spiking neural networks.
Neural Computation 30, 1514–1541.
fulltext | preprint | code - Zenke, F.*, Poole, B.*, and Ganguli, S. (2017).
Continual Learning Through Synaptic Intelligence.
Proceedings of the 34th International Conference on Machine Learning (ICML), pp. 3987–3995.
fulltext | preprint | code | talk - Zenke, F., Gerstner, W., and Ganguli, S. (2017).
The temporal paradox of Hebbian learning and homeostatic plasticity.
Current Opinion Neurobioloy 43, 166–176.
fulltext | preprint - Zenke, F. and Gerstner, W. (2017).
Hebbian plasticity requires compensatory processes on multiple timescales.
Philosophical Transactions of the Royal Society B 372, 20160259.
fulltext | preprint | supplement - Gilson, M., Savin, C., and Zenke, F. (2015).
Editorial: Emergent neural computation from the interaction of different forms of plasticity.
Frontiers in Computational Neuroscience 9, 145.
fulltext | ebook download of the entire issue - Zenke, F., Agnes, E. J., Gerstner, W. (2015).
Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks.
Nature Communications 6. doi: 10.1038/ncomms7922
fulltext | supplement | code - Ziegler, L., Zenke, F., Kastner, D.B., Gerstner, W. (2015).
Synaptic Consolidation: From Synapses to Behavioral Modeling.
Journal of Neuroscience 35, 1319–1334.
fulltext - Zenke, F. and Gerstner, W. (2014).
Limits to high-speed simulations of spiking neural networks using general-purpose computers.
Frontiers in Neuroinformatics 8, 76.
fulltext | code - Zenke, F., Hennequin, G., Gerstner, W. (2013).
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector.
PLoS Computational Biology 9, e1003330.
fulltext - Lütcke, H., Gerhard, F., Zenke, F., Gerstner, W., Helmchen, F. (2013).
Inference of neuronal network spike dynamics and topology from calcium imaging data.
Frontiers in Neural Circuits 7.
fulltext - Vogels, T.P., Froemke, R.C., Doyon, N., Gilson, M., Haas, J.S., Liu, R., Maffei, A., Miller, P., Wierenga, C., Woodin, M.A., Zenke, F., Sprekeler, H. (2013).
Inhibitory Synaptic Plasticity – Spike timing dependence and putative network function.
Frontiers in Neural Circuits 7.
fulltext - Vogels, T.P., Sprekeler, H., Zenke, F., Clopath, C., Gerstner, W. (2011).
Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks.
Science 334, 1569 –1573.
fulltext | supplement | video | code