An associate professor in the Computational Neurobiology Laboratory, Sharpee will receive $453,000 over the next five years to fund her proposed study: "Characterizing feature selectivity and invariance in deep neural architectures." In an effort to help elucidate the principles that make robust object recognition possible, she will explore how an organism's neurons are able to demonstrate both "invariance," which produces a similar response to the same object even when observed from different viewpoints, and "selectivity," which requires different responses to potentially quite similar objects. The results of her study will help reveal the common principles of sensory processing in the brain and may ultimately lead to improved designs of artificial recognition systems, including sensory prostheses.
Sharpee received her Ph.D. in Physics at Michigan State University and was a Sloan-Swartz Postdoctoral Fellow at the University of California, San Francisco. In the Computational Neurobiology Laboratory at the Salk Institute, Sharpee and her team work on theoretical principles of how the brain processes information.