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Friedemann Zenke


NEUROBIOLOGY

Friedemann Zenke will be joining the FMI and start the laboratory on June 1st, 2019. He is currently setting up his group and welcomes applications for PhD and postdoc positions.
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Neural dynamics of learning and computation

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.

Additional information:
Personal homepage of Friedemann Zenke


BIOGRAPHY

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


SELECTED PUBLICATIONS


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

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