Overview on Google Scholar
Preprints
- Halvagal, M. S. and Zenke, F. (2022)
The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks.
accepted
preprint | code - 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. (2022)
CONVOLVE: Smart and seamless design of smart edge processors.
preprint
Published articles in peer-reviewed venues
- 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
full text | preprint - Laborieux, A. and Zenke, F. (2022)
Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations.
NeurIPS, 2022.
full text | preprint | code - 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
full text | preprint - 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 - 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
full text | preprint | code - 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 - 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 - 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 - 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.
full text | preprint - Liu, T., and Zenke, F. (2020)
Finding trainable sparse networks through Neural Tangent Transfer.
ICML
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
full text | 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. doi: 10.1038/s41593-019-0520-2
full text - 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 - 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
full text | preprint - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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 - 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
Earlier publications from particle physics
Here is a list of Friedemann’s older publications from particle physics.
Theses
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