The Brain (Inspired) Computing group
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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. |
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