Leader of the group Machine Learning: Peter Grünwald.

Our research group focuses on how computer programs can learn from and understand data, and then make useful predictions based on it. These algorithms integrate insights from various fields, including statistics, artificial intelligence and neuroscience.  

 Machine-learning applications are increasingly part of every aspect of life, from speech recognition on cell phones to illness prediction in healthcare. One common problem is extremely polluted data, for which no single model can provide adequate explanations. At CWI we address this issue with statistical machine learning based on combining predictions from different models and experts in order to achieve reliable conclusions.

We also study how networks of neurons in the brain process information, and how modern deep-learning methods can benefit from neuroscience. We develop novel neural networks, like Deep Adaptive Spiking Neural Networks, and also theoretical models of neural learning and information processing in biology. Applications of our work range from low-energy consumption neural machine learning to neuroprosthetics, to increased insight into the question of how the brain works.



No vacancies currently.


Current events

Symposium on Learning Algorithms for Spiking Neural Networks

  • 2022-12-13T10:15:00+01:00
  • 2022-12-13T13:00:00+01:00
December 13 Tuesday

Start: 2022-12-13 10:15:00+01:00 End: 2022-12-13 13:00:00+01:00

CWI, room L016


10:15 - 11:15 Friedemann Zenke (Friedrich Miescher Institute for Biomedical Research, and University of Basel, Switzerland).
Principles of predictive representation learning in biological neural networks.

11:15 - 11:45 Sebastian Otte (Wilhelm Schickard Institute, University of Tübingen, Germany).
Towards more efficient recurrent learning.

11:45 - 12:00 Break

12:00 - 12:30 Aditya Gilra (Centrum Wiskunde & Informatica, Amsterdam, Netherlands).
Local learning of non-linear dynamics in recurrent spiking neural networks.

12:30 - 13:00 Bojian Yin (Centrum Wiskunde & Informatica, Amsterdam, Netherlands).
Efficient Spiking Neural Network: from lab to real-world.

Please register in advance for this symposium to enable room arrangements. Registration closes on 12th December 2022.

Registration form HERE.

PhD defence Bojian Yin (Machine Learning)

  • 2022-12-14T11:00:00+01:00
  • 2022-12-14T12:00:00+01:00
December 14 Wednesday

Start: 2022-12-14 11:00:00+01:00 End: 2022-12-14 12:00:00+01:00

Eindhoven University Atlas 0.710

Everybody is welcome to attend the public lecture of Bojian Yin of his thesis entitled 'Efficient Spiking Neural Networks'.

Promoters: prof. dr. S.M. Bohte and prof.dr. H. Corporaal

Co-promotor: dr. F. Corradi

PhD defence Lynn Sörensen (Machine Learning)

  • 2023-01-17T15:00:00+01:00
  • 2023-01-17T16:00:00+01:00
January 17 Tuesday

Start: 2023-01-17 15:00:00+01:00 End: 2023-01-17 16:00:00+01:00

UvA Agnietenkapel, Oudezijds Voorburgwal 231, Amsterdam

Everybody is welcome to attend the public defence of Lynn Sörensen of her thesis entitled 'Deep Neural Network Models of Visual Cognition'.

Promotores:  H. Steven Scholte (UvA), Heleen A. Slagter (VU)
co-promotor: Sander M. Bohté (CWI, RUG)


Associated Members



Current projects with external funding

  • Enabling Personalized Interventions (EPI)
  • Human Brain Project - SGA3 (HBP-SGA3)
  • Efficient Models of Decision-Making for Asseing Cognitive Processing States (None)
  • Increasing Scientific Efficiency with Sequential Methods (pre-proposal) (None)
  • Perceptive acting under uncertainty: safety solutions for autonomous systems (None)
  • Spiking Neural Networks research program

Related partners

  • Katholieke Universiteit Nijmegen
  • Philips
  • KPMG
  • SURFsara B.V.
  • Technische Universiteit Eindhoven
  • Technische Universiteit Delft
  • Universiteit van Amsterdam
  • Vrije Universiteit Amsterdam