The Brain (Inspired) Computing group

In our group we study fundamental properties of probabilistic computational models of information processing, particulary in the brain (e.g. in the Bayesian brain / predictive processing accounts) and inspired by the brain (e.g. in neuromorphic computations). Our goal is to understand the potential and limitations of resource-bounded probabilistic computations, particularly with respect to time and energy usage, in order to better understand natural intelligence and realise artificial cognitive systems.

Our primary research methods are computational and formal modeling, algorithm design, and mathematical analysis of information representation and processing in frameworks such as Bayesian networks and spiking neural networks. In our research we value explanations that are both mathematically rigorous and biologically plausible; that scale up to 'real world settings' embodied and embedded in a realistic environment, and that 'advance the theory' as well as 'explain the data'. We are committed to fostering an open, safe, and inclusive research environment for our group members.
Group members
  • Johan Kwisthout - associate principle investigator
  • Leila Bagheriye - Postdoc
  • Nils Donselaar - PhD candidate
  • Danaja Rutar - PhD candidate
  • Erwin de Wolff - PhD candidate
  • Borislav Sabev - MSc AI thesis project
  • Daniel Anthes - Research assistant
  • Arne Diehl - Research assistant
  • Ellen Schrader - Research assistant
Alumni
Current projects
Previous projects
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Have a look at our group's who-is-who poster!