We conduct research on the computational principles that underlie natural and artificial intelligence. Intelligent behavior is learned by adapting to the environment; it requires integration of sensory data with prior knowledge; and it must be robust to noise.

The research goal is to provide theoretical insights, models and methods that address these issues combining methods from machine learning and neuroscience. Particular topics are approximate methods for probabilistic ′Bayesian′ inference, control theory, neural networks and data analysis. The research results are applied in diverse fields outside of science together with our spin-off company SMART Research BV.