Friedemann Zenke, PhD
https://fzenke.net
Principal Investigator
Fabian Mikulasch — Post-doc (since 2025)
PhD in Computational Neuroscience at Max-Planck Institute for Dynamics and Self-organization. MSc in Complex Systems, Chalmers, Göteborg. BSc in Cognitive Science, Uni Tübingen. I am interested in the principles of representation and world model learning in artificial and biological neural systems.
Mattias Nilsson, PhD — Post-doc (since 2023)
Ph.D. in Neuromorphic Computing and M.Sc. in Engineering Physics from Luleå University of Technology, Sweden. I am interested in spiking neural networks and their application on emerging neuromorphic hardware for efficient computation, such as for edge AI applications.
Rory Bedford — PhD student (starting 2025) co-advised with Rainer Friedrich
https://rory-bedford.github.io/
MSc in Machine Learning from UCL, BA in Physics from Oxford University. I’m interested in modelling biological neural networks, and in drawing insights from connectomics and calcium imaging datasets.
Peter Buttaroni — PhD student (since 2022)
MSc in Quantitative Finance at Luiss Guido Carli (IT) and MSc in AI from the Università della Svizzera Italiana (CH). I am interested in Reinforcement Learning algorithms and how biological principles can be used to improve artificial neural networks.
Julia Gygax— PhD student (since 2022)
https://jgygax.github.io
BSc in Electrical Engineering and Information Technology, ETHZ. MSc in Biomedical Engineering from ETHZ. I am interested in how concepts of biological neural networks can be applied to artificial neural networks and in training of spiking neural networks.
Manu Srinath Halvagal — PhD student (since 2020)
https://mshalvagal.github.io
M.Sc, Microengineering with a minor in Computational Neuroscience, EPFL and B.Tech, Electrical Engineering, IIT Madras. I am interested in the mechanisms of unsupervised and self-supervised perceptual learning in networks of biological neurons, and the role of top-down connections therein. I am also interested in exploring whether we could use these principles to improve machine learning algorithms.
Michael Hauri — PhD student (starting 2025)
BSc in Electrical and Electronics Engineering, EPFL. MSc in Neuro-X, EPFL. I am interested in modeling the sensorimotor system using reinforcement learning and exploring how internal representations shape perception and motor control.
Ashena Gorgan Mohammadi — PhD student (since 2023)
https://ashenagmohammadi.netlify.app
MSc in Computer Science from University of Tehran. My main interest is to develop bio-plausible intelligent systems in broad terms. More specifically, I am interested in how we can simulate predictive and continual learning in a spiking model of columnar architecture of the neocortex.
Thomas Nortmann — PhD student (since 2025)
BSc and MSc in Cognitive Science from Osnabrück University, Germany. I am interested in self-supervised learning for visual perception and the underlying goals needed for good internal representations to emerge. My work focuses on commonalities and differences of early perceptual representations in biological and artificial neural networks.
Julian Rossbroich — PhD student (since 2020)
Joint MSc in Neuroscience from Université Laval and Université de Bordeaux. My main interest concerns the neuronal signatures of learning and memory. Specifically, I am interested in how cortical circuits compute predictions of sensory inputs, and how such predictive processing is involved in learning and plasticity.
Marco Tasca — Intern (starting 2025)
BSc in Computer Science Engineering and MSc in Data Science and Engineering from Politecnico di Torino. I am passionate about exploring the interplay between AI and neuroscience, leveraging biological primitives to inspire smarter AI—particularly through spiking neural networks—and using AI to deepen our understanding of the brain.
Collaborators and co-advised students
Claire Meissner-Bernard, Post-doc
with Rainer Friedrich, FMI.
Simon Narduzzi, PhD student
co-advised by Shih-Chii Liu, Institute of Neuroinformatics (UZH/ETHZ) and L. Andrea Dunbar, CSEM.
MSc in computer science and computational neuroscience (EPFL). My research focuses on developing efficient neural networks for ultra-low power neuromorphic hardware.
Alumni
- Julius Wurzler (2024) → MSc in Bioinformatics, University of Tübingen
- Aaron Spieler (2024) → MSc in Neural Information Processing, University of Tübingen
- Tengjun Liu (2023–2024) → PhD student at Zhejiang University
- Axel Laborieux (2021–2024) → Research scientist, Huawei Research
- Jeremias Seitz (2022–2024)
- Manvi Agarwal (2021–2022) → PhD student, Télécom Paris, Institut Polytechnique de Paris
- Siegfried Schwartz (2022) → CS student (bachelor) at TU Vienna
- Peter Buttaroni (2022) → PhD student, Zenke Lab
- Yue Kris Wu (2019–2022) → PhD student, TU Munich
- Nikolaos Papanikolaou (2021–2022) → PhD student, University of Stuttgart, Germany
- Guillermo Martin Sanchez (2021–2022) → PhD student, Champalimaud
- Julia Gygax (2021) → PhD student, Zenke Lab
- Matthias Depoortere (2020) → AI/ML Engineer at GSK Vaccines
- Tianlin Liu (2019–2020) → PhD student, University of Basel
Former collaborators and external students
- Bastian Eichenberger, FMI, PhD student (2020–2023), co-advised with Jeff Chao and Guillaume Diss → Independent consultant
- Luke Taylor, University of Oxford, PhD student (2019–2024), co-advised with Dr Nicol Harper and Professor Andrew King
- Sebastian Billaudelle, University of Heidelberg, PhD student (2021–2022), co-advised with Johannes Schemmel → Postdoc, INI, Uni and ETH Zurich
- Benjamin Cramer, University of Heidelberg, PhD student (2019–2022), co-advised with Johannes Schemmel → Staff scientist, Bosch Research