Donders Institute for Brain, Cognition and Behaviour
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Theme 4: Brain Networks and Neuronal Communication

Machine Learning (iCIS)

Donders Institute for Brain, Cognition and BehaviourThe high-level ambition of the Machine Learning group at the Institute for Computing and Information Sciences is to work towards "bounded rational" machine learning: machine learning methods that properly take into account all information available (both data and prior knowledge) and then provide an optimal answer, given finite resources and computation time.

The Machine Learning group aims to show that such algorithms (indeed) improve the state-of-the-art, both in theory and in practice. By applying machine learning methods to problems in other scientific domains, such as (cognitive) neuroscience and bioinformatics, the machine learning group wishes to contribute to the progress in these other disciplines.

We follow two approaches: top-down, through a Bayesian approach, and bottom-up, by designing clever heuristics. The Bayesian approach has - in theory - many desirable properties, such as consistency, coherence, and optimality, but in practice leads to computationally intractable problems. More heuristic approaches are designed to be tractable in the first place, but are more difficult to interpret in terms of objective measures of optimality. Having expertise on both sides, we strive to combine the best of both worlds.

We hook up with other scientists, at the Donders Institute and elsewhere, to work together on the analysis of their data to extract new scientific knowledge. This does not only provide a test-bed for existing machine learning methods, but also provides inspiration for new ones.

Name: Tom Heskes
Telephone: 024-3652696
Visiting address: Department of Intelligent Systems
Faculty of Science
Heyendaalseweg 135
6525 AJ Nijmegen
The Netherlands
Postal address: Department of Intelligent Systems
Faculty of Science
P.O. Box 9010
6500 GL Nijmegen
The Netherlands
Key grants and prizes
  • VICI Tom Heskes)
  • NWO Open Competition MacBrain
  • NWO Complexity SYNCOBE
  • NWO Open Competition More Confidence in Causal Discovery
  • VENI Joris Mooij
Key publications
  • Max Hinne, Tom Heskes, Christian Beckmann, and Marcel van Gerven. Bayesian inference of structural brain networks. NeuroImage, 66:543-552, 2013.
  • Niels Cornelisse, Evgeni Tsivtsivadze, Marieke Meijer, Tjeerd Dijkstra, Tom Heskes, and Matthijs Verhage. Molecular machines in the synapse: overlapping protein sets control distinct steps in neurosecretion. PLoS Computation Biology, 8:e1002450, 2012
  • Tom Claassen and Tom Heskes. A Bayesian approach to constraint based causal inference. In: UAI 2012, Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, pages 207-216, 2012 (best paper award)
  • Twan van Laarhoven, Sander Nabuurs and Elena Marchiori. Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 27:3036-3043, 2011
  • Joris Mooij, Dominik Janzing, Tom Heskes, and Bernhard Schölkopf. On causal discovery with cyclic additive noise models. In: Advances in Neural Information Processing Systems 24 (NIPS*2011, pages 639-647, 2011


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Theme 4:
Natural Computing & Neurotechnology

Research Group
Machine Learning (iCIS)

Affiliated Principal Investigator
Prof. Tom Heskes

Group members

Scientific staff

Associate Professor
Dr. Elena Marchiori

Assistant Professor
Dr. Janos Sarbo

Dr. Perry Groot
Dr. Tom Claassen

Daniel Kühlwein
Wout Megchelenbrink
Max Hinne
Jonce Dimov
Elena Sokolova
Christiaan de Leeuw
Ridho Rahmadi
Mohsen Ghafoorian
Twan van Laarhoven