We are interested how the brain and artificial systems generate intelligent behavior, and how self-organization allows them to robustly learn and perform these behaviors.
Currently, a major focus of the lab lies on inhibitory neural circuits in the brain. Inhibition is often primarily thought of as a stabilizer of neuronal networks. The richness of the observed inhibitory circuitry suggests, however, that it serves a much richer set of functions. We explore candidates for these functions using computational network models, because they allow to take experimental observations on the neural or microcircuit level to a functional or systems level. Because these networks are often rich in parameters, we often investigate in parallel which forms of neuronal plasticity can self-tune the network to achieve a hypothesized function.
We are also interested in self-organization in the context of cognitive processes and behavior, specifically how systems can learn from experience, form and maintain memories and acquire useful sensory representations.
More information about past and present projects can be found below and on the pages of the individual members of the team.