Overview on Google scholar

  1. Halvagal, M.S., and Zenke, F. (2022).
    The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks.
  2. Cramer, B., Billaudelle, S., Kanya, S., Leibfried, A., Grübl, A., Karasenko, V., Pehle, C., Schreiber, K., Stradmann, Y., Weis, J., Schemmel, J., Zenke, F. (2022).
    Surrogate gradients for analog neuromorphic computing.
    PNAS 119.
    fulltext | preprint | code
  3. Wu, Y.K., and Zenke, F. (2021).
    Nonlinear transient amplification in recurrent neural networks with short-term plasticity.
    eLife 10, e71263.
    fulltext | preprint | code
  4. 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 | preprint
  5. 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.
  6. 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
  7. Zenke, F., and Neftci, E.O. (2021).
    Brain-Inspired Learning on Neuromorphic Substrates.
    Proceedings of the IEEE 1–16.
    fulltext | preprint
  8. 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
  9. 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
  10. 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
  11. 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.
    fulltextpreprint | code
  12. 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.
  13. 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.
  14. Zenke, F. and Ganguli, S. (2018).
    SuperSpike: Supervised learning in multi-layer spiking neural networks.
    Neural Computation 30, 1514–1541.
    fulltext | preprint | code
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. Ziegler, L., Zenke, F., Kastner, D.B., Gerstner, W. (2015).
    Synaptic Consolidation: From Synapses to Behavioral Modeling.
    Journal of Neuroscience 35, 1319–1334.
  21. 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
  22. Zenke, F., Hennequin, G., Gerstner, W. (2013).
    Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector.
    PLoS Computational Biology 9, e1003330.
  23. 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.
  24. 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.
  25. 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

Here is a list of Friedemann’s older publications mostly from particle physics.