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. Overall, the premise for our work is that animal behavior provides clues about natural intelligence, i.e., the algorithms for solving a problem.
We also explore the relevance of our biological insights for natural language processing (NLP). We want to explore possible parallels between human language and birdsong and test the relevance of evolutionary vocal learning mechanisms for the processing of human language. We direct our NLP outreach efforts to helping researchers in assimilating the scientific literature and in generating scientific arguments.
We use our mixed educational backgrounds 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 processing natural language.