Friedemann Zenke
Group News
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Feb 17, 2022 AI Overcomes Stumbling Block on Brain-Inspired Hardware |
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Dec 23, 2021 How neurons that wire together fire together |
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Nov 19, 2021 Eccellenza Fellowship for Friedemann Zenke |
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Nov 2, 2020 Building artificial neural networks inspired by the brain |
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Jun 24, 2019 In conversation with our two new group leaders |
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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.
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, 571-575Zenke, 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, 51-63Zenke, F. and Ganguli, S. (2018) SuperSpike: Supervised learning in multi-layer spiking neural networks
Neural Comput 30, 1514-1541. doi: 10.1162/neco_a_01086Gjoni, 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/JP276012Zenke, 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, 166-176Zenke, F. and Gerstner, W. (2017) Hebbian plasticity requires compensatory processes on multiple timescales
Phil Trans R Soc B 372, 20160259. doi: 10.1098/rstb.2016.0259Zenke, 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/ncomms7922Ziegler, 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.3989-14.2015Zenke, 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.2014.00076Zenke, 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.1003330Lütcke, 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.2013.00201Vogels, 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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