Friedemann Zenke

Group News

Feb 17, 2022
AI Overcomes Stumbling Block on Brain-Inspired Hardware
Dec 23, 2021
How neurons that wire together fire together
Nov 19, 2021
Eccellenza Fellowship for Friedemann Zenke
Nov 2, 2020
Building artificial neural networks inspired by the brain
Jun 24, 2019
In conversation with our two new group leaders
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Group Website
Computational Neuroscience Initiative Basel

Friedemann Zenke

Computational Neuroscience

The overall aim of our research group is to understand the principles that underlie memory formation and information processing in biological neural networks. To this end, we build neural network models with experience-dependent plasticity and study how specific function can emerge through the orchestrated interplay of different plasticity mechanisms.

Mathematical models allow us to integrate and conceptualize the vast amounts of data generated by modern experiments. Moreover, models offer a unique vantage point over the high-dimensional complexity encountered in neural networks because they allow us to control factors such as noise and partial observability, which can confound experimental data. Thus, models enable us to efficiently explore new theories and help generate experimentally testable predictions.

Our modeling efforts focus on how both the structure and function of neural networks are shaped by plasticity. Specifically, we take a three-fold approach, which combines simulations, theory, and data analysis. First, to simulate large rate-coding and spiking neural networks with plasticity, we rely on high-performance computing and machine learning techniques. Second, to interpret and understand the dynamics in our models, we employ a variety of analytical tools from dynamical systems, control theory and statistical physics. Finally, to compare the high-dimensional dynamics of models with neurobiological data, we work closely with our experimental colleagues on the development and the application of practical dimensionality reduction techniques.

Friedemann Zenke

This is a list of selected publications from this group. For a full list of publications, please visit our Publications page and search by group name.

Zenke, F., Bohté, S.M., Clopath, C., Comsa, 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

Zenke, F., and Neftci, E.O. (2021) Brain-Inspired Learning on Neuromorphic Substrates.

Proceedings of the IEEE 1-16.

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.

Liu, T., and Zenke, F. (2020) Finding trainable sparse networks through Neural Tangent Transfer.

Proceedings of the 37th International Conference on Machine Learning (ICML)

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

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

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. doi: 10.1113/JP276012

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
* Co-first author

Zenke, F., Gerstner, W., Ganguli, S. (2017) The temporal paradox of Hebbian learning and homeostatic plasticity

Curr Opin Neurobiol 43, 166-176

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

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

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

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

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

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

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
* Co-first author

Full list of publications
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Group leader

In current position since 2019
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PhD students

In current position since 2021
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In current position since 2022
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In current position since 2020
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In current position since 2020
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Postdoctoral fellows

In current position since 2021
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In current position since 2022
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PhD students

Yue Wu (2019-2021)
Tianlin Liu (2019-2020)


Peter Buttaroni (2022)
Nikolaos Papanikolaou (2021-2022)
Guillermo Martin Sanchez (2021-2022)
Julia Gygax (2021)
Matthias Depoortere (2020)


PhD, School of Computer and Communication Sciences, EPF Lausanne, Switzerland
Diplom in Physics, Helmholtz-Institute for Radiation- and Nuclear Physics, University of Bonn, Germany
Physics, Exchange Program, Australian National University, Canberra, Australia

Positions held

Junior Group Leader, Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
Sir Henry Wellcome Postdoctoral Fellow, University of Oxford, UK
Postdoctoral Fellow, Stanford University, USA


Postdoctoral Fellowship, Wellcome Trust
Postdoctoral Fellowship, Swiss National Science Foundation
Teaching Award, The School of Computer and Communication Sciences, EPFL
PhD Fellowship, Marie Curie Actions
Member, Bonn-Cologne Graduate School of Physics and Astronomy
Fellowship, German Academic Exchange Service (DAAD)