Overview on Google Scholar


Taylor, L., Zenke, F., King, A. J., and Harper, N. S. (2024)
Temporal prediction captures key differences between spiking excitatory and inhibitory V1 neurons.

Gygax, J. and Zenke, F. (2024)
Elucidating the theoretical underpinnings of surrogate gradient learning in spiking neural networks.

Meissner-Bernard, C., Jenkins, B., Rupprecht, P., Bouldoires, E.A., Zenke, F., Friedrich, R.W., Frank, T. (2024)
Computational functions of precisely balanced neuronal assemblies in an olfactory memory network.

Taylor, L., Zenke, F., King, A. J., and Harper, N. S. (2024)
Temporal prediction captures retinal spiking responses across animal species.
preprint | code

Meissner-Bernard, C., Zenke, F., Friedrich, R.W. (2023)
Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex.
eLife reviewed preprint doi: 10.7554/eLife.96303.1

Published articles in peer-reviewed venues

Laborieux, A. and Zenke, F. (2024)
Improving equilibrium propagation without weight symmetry through Jacobian homeostasis.
ICLR, in press.
full text | preprint | code

Rossbroich, J. and Zenke, F. (2023)
Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity.
NeurIPS doi: 10.48550/arXiv.2310.19614
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Halvagal, M. S.*, Laborieux, A.*, and Zenke, F. (2023)
Implicit variance regularization in non-contrastive SSL.
NeurIPS doi: 10.48550/arXiv.2212.04858
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Halvagal, M. S. and Zenke, F. (2023)
The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks.
Nature Neuroscience doi: 10.1038/s41593-023-01460-y
full text | preprint | code | erratum

Payvand, M., Neftci, E., Zenke, F. (2023)
Editorial: Focus Issue on Machine Learning for Neuromorphic Engineering.
Neuromorphic Computing and Engineering doi: 10.1088/2634-4386/acee1a
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Gomony, M., Putter, F., Gebregiorgis, A., Paulin, G., Mei, L., Jain, V., Hamdioui, S., Sanchez, V., Grosser, T., Geilen, M., Verhelst, M., Zenke, F., Gurkaynak, F., Bruin, B., Stuijk, S., Davidson, S., De, S., Ghogho, M., Jimborean, A., Eissa, S., Benini, L., Soudris, D., Bishnoi, R., Ainsworth, S., Corradi, F., Karrakchou, O., Güneysu, T., Corporaal, H. (2023)
CONVOLVE: Smart and seamless design of smart edge processors.
2023 Design, Automation & Test in Europe Conference & Exhibition (DATE) doi: 10.23919/DATE56975.2023.10136926
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Halvagal, M. S.*, Laborieux, A.*, and Zenke, F. (2022)
An eigenspace view reveals how predictor networks and stop-grads provide implicit variance regularization.
NeurIPS Self-Supervised Learning Workshop
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Laborieux, A. and Zenke, F. (2022)
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations.
NeurIPS, 2022.
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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

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
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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
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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

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
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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.
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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

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 | erratum

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
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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, 2020.
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Liu, T., and Zenke, F. (2020)
Finding trainable sparse networks through Neural Tangent Transfer.
full text | 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 Signal Processing Magazine, 36, 51–63. doi: 10.1109/MSP.2019.2931595
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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
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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
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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

Zenke, F.*, Poole, B.*, and Ganguli, S. (2017)
Continual Learning Through Synaptic Intelligence.
ICML, 70, 3987-3995.
full text | 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. doi: 10.1016/j.conb.2017.03.015
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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
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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.
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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
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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
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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
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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
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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
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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
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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

Book chapters

Zenke, F., and Yadava, K. “Artificial Intelligence: A Brief Introduction for Non-Experts on the Technological Advances That Are Bringing Smart Devices into Our Lives.” In Robots and Gadgets: Aging at Home, edited by Félix Pageau, Tenzin Wangmo, and Emilian Mihailov, 7–24. Les Presses de l’Université Laval, 2024.

Earlier publications from particle physics

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


PhD thesis

Title: “Memory formation and recall in recurrent spiking neural networks” Advisor: Wulfram Gerstner – EPF Lausanne, 2014 doi:10.5075/epfl-thesis-6260 fulltext (mirror)

Diploma thesis (“Diplomarbeit”)

Title: “A new avalanche photodiode readout for the Crystal Barrel experiment” Advisor: Reinhard Beck – University of Bonn, 2009 fulltext