Facial Recognition, How the Genome Codes for It

Infants are innately attracted to face-like objects, which suggests they know what a face looks like without ever having seen one. This sort of innate, complex pattern recognition is critical for much of human behavior. Deficits in these abilities are associated with disorders such as autism and neurodegenerative diseases, as well as the damage suffered after stroke. Understanding the genetic features that underpin the development of innate recognition circuits could provide new avenues for treating these disorders.

With this NIH Director’s New Innovator Award, Michael J. Sheehan, Neurobiology and Behavior, is asking how the genome encodes instructions for the neural circuits that innately recognize specific, complex patterns, such as a face. One of the challenges in this research area has been finding the right models for study. Sheehan’s group has identified a unique species of paper wasp, Polistes fuscatus, which has highly variable facial patterns that they use to recognize individual nestmates within their small societies. Their closely related sister species, however, lack specialized facial processing. By comparing the genomes, Sheehan’s group is probing the genomic basis for the skill’s evolution.

The studies use a genome-wide approach that will pinpoint the genomic loci involved in specifying circuit development and the features that encode information in these circuits. Along the way, Sheehan hopes to establish this unique species of wasp as a powerful invertebrate model for the genomics and neurobiology of complex social behaviors. Most importantly, the research will contribute a deeper and richer understanding of how genomes encode the design of neural circuits for specific recognition tasks.

NIH Award Number: 1DP2GM128202-01

Cornell Researchers

Funding Received

$2.4 Million spanning 5 years

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