We are interested in the neurobiological algorithms inherent in vocal learning, especially of birdsong. To research vocal learning experimentally, we build novel observatories for groups of songbirds and to cope with the obtained massive data sets we adopt methods of statistical inference. The inferred song learning strategies we examine in the context of reinforcement learning theory, which is a modeling framework that is well matched to the organization of the brain.

We also explore the relevance of our biological insights for natural language processing (NLP). We want to test the relevance of vocal learning mechanisms created by evolution in songbirds for processing of human language, with particular consideration of scientific texts and the generation of scientific arguments.

Overall, the premise for our work is that animal behavior provides clues about natural intelligence, i.e., the algorithms for solving a problem. We are a team of mixed backgrounds who want to advance knowledge at the boundary between biology and engineering, encompassing natural behavior, the organization of the songbird brain, computational theories of vocal learning, and the creation of new methods for natural language processing.

Our research