The Brain (Inspired) Computing group
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Our group studies conceptual and computational aspects of information processing in the human brain as well as in hardware systems inspired by the brain's architecture. This work is done in two major research lines: "Predictive Processing" and "Neuromorphic computing". In the first research line we study fundamental properties of the Predictive Processing accounts in (cognitive) neuroscience. We are particularly interested in the mechanisms, algorithms, and information structures needed to 'implement' higher cognition within the Predictive Processing account; how these algorithms can be tractable relative to the brain's resources and environmental constraints, and how these information structures are developed. In the second research line we are interested in novel ways of information storage and processing on new hardware architectures inspired by the brain. We aim to design energy-efficient algorithms for neuromorphic hardware and investigate which problems are inherently 'energy-hard' on such architectures. We employ a diverse array of scientific methodologies, including formal mathematical proofs in complexity theory, behavioral developmental research, conceptual analysis, computational modeling, algorithm design and analysis, and explorative developmental robotics. 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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