Publications

Overview on Google Scholar

Preprints and accepted papers

  1. Laborieux, A. and Zenke, F. (2022)
    Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations.
    NeurIPS, accepted.
    preprint | code
  2. Halvagal, M. S. and Zenke, F. (2022)
    The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks.
    preprint | code

Published work

  1. Halvagal, M. S.*, Laborieux, A.*, and Zenke, F. (2022)
    An eigenspace view reveals how predictor networks and stop-grads provide implicit variance regularization.
    NeurIPS 2022 Workshop: Self-Supervised Learning
    full text
  2. Rossbroich, J., Gygax, J., and Zenke, F. (2022)
    Fluctuation-driven initialization for spiking neural network training.
    Neuromorphic Computing and Engineering doi: 10.1088/2634-4386/ac97bb
    full text | preprint | postprint | code
  3. Muller-Cleve, S.F., Fra, V., Khacef, L., Pequeno-Zurro, A., Klepatsch, D., Forno, E., Ivanovich, D.G., Rastogi, S., Urgese, G., Zenke, F., and Bartolozzi, C. (2022)
    Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware.
    Frontiers in Neuroscience, 16. doi: 10.3389/fnins.2022.951164
    full text | preprint
  4. 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. doi: 10.1073/pnas.2109194119
    full text | preprint | code
  5. Cramer, B., Stradmann, Y., Schemmel, J., and Zenke, F. (2022)
    The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural Networks.
    IEEE Transactions on Neural Networks and Learning Systems, 33, 2744–2757. doi: 10.1109/TNNLS.2020.3044364
    full text | preprint | data | code
  6. Wu, Y.K., and Zenke, F. (2021)
    Nonlinear transient amplification in recurrent neural networks with short-term plasticity.
    eLife, 10, e71263. doi: 10.7554/eLife.71263
    full text | preprint | code
  7. 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, 24, 1010–1019.
    full text | preprint
  8. Zenke, F., Bohté, S.M., Clopath, C., Comşa, I.M., Göltz, J., Maass, W., Masquelier, T., Naud, R., Neftci, E.O., Petrovici, M.A., Scherr, F., and Goodman, D. F. M. (2021)
    Visualizing a joint future of neuroscience and neuromorphic engineering.
    Neuron, 109, 571–575.
    full text
  9. Zenke, F., and Vogels, T.P. (2021)
    The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks.
    Neural Computation, 33 (4), 899–925. doi: 10.1162/neco_a_01367
    full text | preprint
  10. Zenke, F., and Neftci, E.O. (2021)
    Brain-Inspired Learning on Neuromorphic Substrates.
    Proceedings of the IEEE, 109 (5), 935-950. doi: 10.1109/JPROC.2020.3045625
    full text | preprint
  11. 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.
    full text | preprint
  12. Liu, T., and Zenke, F. (2020)
    Finding trainable sparse networks through Neural Tangent Transfer.
    ICML
    full text | preprint | code
  13. 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 Signal Processing Magazine, 36, 51–63. doi: 10.1109/MSP.2019.2931595
    full text | preprint | code
  14. 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. doi: 10.1038/s41593-019-0520-2
    full text
  15. 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.
    Journal of Physiology, 20, 4945-4967. doi: 10.1113/JP276012
    full text
  16. Zenke, F. and Ganguli, S. (2018)
    SuperSpike: Supervised learning in multi-layer spiking neural networks.
    Neural Computation, 30, 1514–1541. doi: 10.1162/neco_a_01086
    full text | preprint | code
  17. Zenke, F.*, Poole, B.*, and Ganguli, S. (2017)
    Continual Learning Through Synaptic Intelligence.
    ICML, 70, 3987-3995.
    full text | preprint | code | talk
  18. Zenke, F., Gerstner, W., and Ganguli, S. (2017)
    The temporal paradox of Hebbian learning and homeostatic plasticity.
    Current Opinion Neurobioloy, 43, 166–176. doi: 10.1016/j.conb.2017.03.015
    full text | preprint
  19. Zenke, F. and Gerstner, W. (2017)
    Hebbian plasticity requires compensatory processes on multiple timescales.
    Philosophical Transactions of the Royal Society B, 372, 20160259. doi: 10.1098/rstb.2016.0259
    full text | postprint | code
  20. 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.
    full text
  21. 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, 6922. doi: 10.1038/ncomms7922
    full text | code
  22. Ziegler, L., Zenke, F., Kastner, D.B., Gerstner, W. (2015)
    Synaptic Consolidation: From Synapses to Behavioral Modeling.
    Journal of Neuroscience, 35, 1319–1334. doi: 10.1523/JNEUROSCI.3989-14.2015
    full text
  23. Zenke, F. and Gerstner, W. (2014)
    Limits to high-speed simulations of spiking neural networks using general-purpose computers.
    Frontiers in Neuroinformatics, 8, 76. doi: 10.3389/fninf.2014.00076
    full text | code
  24. Zenke, F., Hennequin, G., Gerstner, W. (2013)
    Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector.
    PLoS Computational Biology, 9, e1003330. doi: 10.1371/journal.pcbi.1003330
    full text
  25. 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. doi: 10.3389/fncir.2013.00201
    full text
  26. 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. doi: 10.3389/fncir.2013.00119
    full text
  27. 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. doi: 10.1126/science.1211095
    full text | code | video

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