Benjamin Grewe
Also known as Benjamin F. Grewe
Benjamin Grewe is a neuroinformatics professor whose research connects biological learning with artificial intelligence. He studied physics in Kiel and Heidelberg, earned a neuroinformatics PhD at ETH Zurich, and developed high-speed calcium-imaging methods for neural activity. At Stanford, he studied how neural networks learn and store memories before joining ETH as an assistant professor in 2016. He coauthored a brain-inspired continual-learning method, became Associate Professor of Neuronal Learning and Intelligent Systems in 2024, and serves in the ETH AI Center's Core Faculty.
- Now at
- ETH Zurich and University of Zurich
- Badges
Timeline
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2001–2010
Physics and a PhD
He grows up in Schleswig-Holstein and studies physics at Kiel from 2001 to 2003 and at Heidelberg from 2003 to 2007. He completes a PhD in neuroinformatics at ETH Zurich in 2010, researching high-speed acousto-optic imaging of neural activity in the intact mouse brain.
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2010–2011
Near-millisecond imaging
In 2010, he coauthors “High-speed in vivo calcium imaging reveals spike trains in neuronal networks with near-millisecond precision.” In 2011, he coauthors “Fast two-layer two-photon imaging of neuronal cell populations using an electrically tunable lens.”
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2011–2016
Memory studies at Stanford
From 2011 to 2016, he works as a postdoctoral researcher in Mark Schnitzer's Stanford lab. There he studies learning-induced changes in neural network codes with miniaturized microscopes that image freely moving animals.
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October 2016
Assistant professor at ETH
In October 2016, he joins ETH Zurich's Institute of Neuroinformatics as an assistant professor. His research focuses on biological and artificial intelligence.
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December 2022
Brain-inspired continual learning
In December 2022, he coauthors a task-free continual-learning method that combines sparse representations, deep feedback control and recurrent connections. The approach learns multiple tasks without task-boundary information.
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2024
Associate professor of neural learning
In 2024, ETH appoints him as Associate Professor of Neuronal Learning and Intelligent Systems within the Institute of Neuroinformatics.
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March 2026
Modular learning systems
In March 2026, he describes systems that learn separate modules for concepts such as gravity or individual bodies, then combine those modules to model new situations.
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As of October 2026
Professor in the ETH AI Center
As of October 2026, he is a professor in the ETH AI Center's Core Faculty. His research interests include computational neuroscience, learning algorithms, reinforcement learning and embodied learning.
Career
- Since 2024ETH ZurichAssociate Professor of Neuronal Learning and Intelligent Systems, Core Faculty, ETH AI Center
- NowUniversity of ZurichProfessor of Neuroinformatics
- 2011–2016Stanford UniversityPostdoctoral Researcher
Education
- 2010ETH ZurichPhD in Neuroinformatics
- 2007Heidelberg UniversityDiploma in Physics
- 2003Kiel UniversityIntermediate Diploma in Physics
Colleagues
Sources and credits
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