The Pfenning laboratory for neurogenomics was awarded an NSF Career award: Machine Learning Approaches to Understanding Molecular Mechanisms Underlying Convergent Evolution of Vocal Learning Behavior.
The ability to perform a variety of complex behaviors, like human speech, is encoded in the billions of nucleotides that make up the genome of an organism. Although speech itself is uniquely human, vocal learning, the ability to modify vocal output as a result of experience, has evolved independently in multiple mammals and birds, including songbirds, parrots, hummingbirds, bats, and whales, as well as humans, uniquely among great apes. During the evolution of each of these species, genome sequence mutations over millions of years have led to differences in the molecular properties of cell types within their brains, allowing for their vocalizations to be learned. This project leverages that diversity across species to take a comparative genomic approach to understanding how vocal learning evolved: what features do the genomes of vocal learning species have in common relative to species without this ability?
To answer that question, this research will adapt a machine learning approach, probabilistic graphical models, to look for common patterns of gene activity across dozens of brain cell types. An additional technique, convolutional neural networks will be applied to search for associated genome sequence patterns across hundreds of mammals. Together, these approaches aim to connect the different levels that vocal learning is studied at, tracing genetic differences in the genome to genes, cell types, brain regions, and neural circuits.
In parallel to the research aims, the project includes the development of educational resources to help these tools be applied more broadly across different cell types, brain regions, species, and behaviors.