Neural computations underlying social behavior in complex sensory environments
Animals often interact in groups. Animal groups constitute complex sensory environments which challenge the brain and engage complex neural computations. This behavioral context is therefore fruitful for understanding how sophisticated neural computations give rise to behavior. However, it is also technically difficult since many of the relevant sensory cues arise from the members of the group and are therefore hard to quantify or control. Consequently, we only incompletely understand how the brain drives complex social behaviors in naturalistic contexts. To uncover the neural computations underlying social behavior in groups, we are using Drosophila, which provides unprecedented experimental access to the nervous system via genetic tools. Drosophila gathers on rotten fruit to feed and mate. Courtship and aggression dominate social interactions and rely on the recognition of sex-specific chemical cues and the production of context-specific acoustic signals. How are these multi-modal cues integrated to control and switch between courtship and aggression? How is unstable and conflicting sensory information resolved to promote stable behavioral strategies? How does sensory processing adapt to socially crowded environments in order to efficiently target behavior at individual members of the group?  These issues will be addressed by combining computational modeling and genetic tools. Using machine learning, we will quantify and model the fine structure of social interactions to identify the social cues that drive behavior. Closed-loop optogenetics and calcium imaging in behaving animals will allow us to test the models and to ultimately reveal how the brain integrates, selects and combines social cues to drive social interactions. This multi-disciplinary approach will uncover the computational principles and mechanisms by which sensory information is processed to drive behavior in the complex sensory environment of animal groups.