Since many years, the research focuses on the computational principles that underlie intelligent behavior in natural systems, or in building artificial intelligence. For instance, intelligent behavior is adaptive and changes on the basis of past seen data; it requires integration of sensory data with prior knowledge; and it must be robust to noise. These problems are fundamental and they occur in many intelligent tasks (cf. vision, motor control, memory, etc.). They are shared by natural and artificial intelligence. The general research goal is to provide theoretical insights, models and approaches that address these issues and is at the interface of machine learning and neuro-science. Particular topics are approximate methods for probabilistic 'Bayesian' inference, control theory, computational neuroscience, data analysis and brain computer interface.

Bayesian methods have a big potential for immediate application in areas outside science. There is a long-standing and quite unique tradition in the SNN group to build such application together with her spin-off companies Smart Research and Promedas.

SNN Adaptive Intelligence is headed by Prof. Dr. Bert Kappen. Depending on how and when you count, our group further consists of about ten researchers, ranging from Master students to postdocs, with indispensable support of one secretary and two programmers.