Friedemann Zenke

Group News

Aug 11, 2022
Self-Taught AI Shows Similarities to How the Brain Works
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
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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.

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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.


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 Vol. 119 | No. 4

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

Wu, Y.K., and Zenke, F. (2021) Nonlinear transient amplification in recurrent neural networks with short-term plasticity.

eLife 10, e71263

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 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

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

Proceedings of the IEEE 1-16.

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 Signal Processing Magazine 36, 51-63

Full list of publications
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Members

Group leader

In current position since 2019
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PhD students

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

In current position since 2021
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Undergraduates

In current position since 2022
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Alumni

PhD students

Manvi Agarwal (2021-2022)
Yue Wu (2019-2021)
Tianlin Liu (2019-2020)

Undergraduates

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

Education

2014
PhD, School of Computer and Communication Sciences, EPF Lausanne, Switzerland
2009
Diplom in Physics, Helmholtz-Institute for Radiation- and Nuclear Physics, University of Bonn, Germany
2006
Physics, Exchange Program, Australian National University, Canberra, Australia

Positions held

2022-
Assistant Professor, University of Basel, Switzerland
2019-
Junior Group Leader, Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
2017-2019
Sir Henry Wellcome Postdoctoral Fellow, University of Oxford, UK
2015-2017
Postdoctoral Fellow, Stanford University, USA

Honors

2022-2027
SNSF Eccellenza Fellowship
2016-2019
Postdoctoral Fellowship, Wellcome Trust
2015-2016
Postdoctoral Fellowship, Swiss National Science Foundation
2012
Teaching Award, The School of Computer and Communication Sciences, EPFL
2010-2014
PhD Fellowship, Marie Curie Actions
2007-2009
Member, Bonn-Cologne Graduate School of Physics and Astronomy
2006
Fellowship, German Academic Exchange Service (DAAD)