Publications

Overview on Google scholar

  • Cramer, B., Stradmann, Y., Schemmel, J., and Zenke, F.
    The Heidelberg spiking datasets for the systematic evaluation of spiking neural networks.
    ArXiv:1910.07407
    preprint | data
  • 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. https://doi.org/10.1109/MSP.2019.2931595
    fulltextpreprint
  • 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.
    Nat Neurosci, 22(11), 1761–1770. doi: 10.1038/s41593-019-0520-2
    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. doi: 10.1113/JP276012
    fulltext
  • Zenke, F. and Ganguli, S. (2018).
    SuperSpike: Supervised learning in multi-layer spiking neural networks.
    Neural Comput 30, 1514–1541. doi: 10.1162/neco_a_01086
    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.
    Curr Opin Neurobiol 43, 166–176. doi: 10.1016/j.conb.2017.03.015
    fulltext | preprint
  • Zenke, F. and Gerstner, W. (2017).
    Hebbian plasticity requires compensatory processes on multiple timescales.
    Phil Trans R Soc B 372, 20160259. doi: 10.1098/rstb.2016.0259
    fulltext | preprint | supplement
  • Gilson, M., Savin, C., and Zenke, F. (2015).
    Editorial: Emergent neural computation from the interaction of different forms of plasticity.
    Front Comput Neurosci 9, 145. doi: 10.3389/fncom.2015.00145
    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 Commun 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.
    J Neurosci 35, 1319–1334. doi:10.1523/JNEUROSCI.3989-14.2015
    fulltext
  • Zenke, F. and Gerstner, W., (2014).
    Limits to high-speed simulations of spiking neural networks using general-purpose computers.
    Front Neuroinform 8, 76. doi: 10.3389/fninf.2014.00076
    fulltext | code
  • Zenke, F., Hennequin, G., Gerstner, W., (2013).
    Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector.
    PLoS Comput Biol 9, e1003330. doi:10.1371/journal.pcbi.1003330
    fulltext
  • Lütcke, H., Gerhard, F., Zenke, F., Gerstner, W., Helmchen, F., (2013).
    Inference of neuronal network spike dynamics and topology from calcium imaging data.
    Front Neural Circuits 7. doi:10.3389/fncir.2013.00201
    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.
    Front Neural Circuits 7. doi:10.3389/fncir.2013.00119
    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. doi:10.1126/science.1211095
    fulltext | supplement | video | code