We research the algorithms inherent in birdsong learning and their relevance for natural language processing. The premise for our work is that behavior provides clues about natural intelligence, i.e., the algorithms for solving a problem.
Retreat on Schatzalp
In December 2018, we spend a few days on Schatzalp to work on Data Analysis. Minus 15 Degrees and gorgeous weather.
The Neural Mechanisms of Birdsong Production and Learning
Similar to speech learning in humans, songbirds learn their songs by adjusting their own vocalizations in reference to a memorized tutor song. They learn their songs in a sensory phase during which they memorize a template of tutor song and in a sensorimotor phase, in which they refine their vocalizations to gradually approximate the tutor song. They achieve this remarkable feat using a highly specialized set of brain areas, termed the song system. Our research is directed at elucidating the various neural mechanisms of song production and song learning by performing experiments in freely behaving and singing birds.
If you want to find out more, have a look at our Projects
Neural Approaches to Natural Language Processing
There are remarkable parallels between birdsong learning and natural language processing. For example, birds use the same strategy for learning their songs that computational linguists use to similar retrieve documents to a given text Natural-Language-Processing.
We are trying to take advantage of parallels between birdsong and natural language, aiming for cross-fertilization in both research fields. One of our strategies is to take observations in songbirds and transfer them to the domain of natural language processing (NLP), where they can help to solve text retrieval problems. We are working on a web application (stay tuned) to help researchers in assimilating the scientific literature and in assessing their own findings and hypotheses within the context of published facts.