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Research shows how neurons learn to pick the most energy-efficient perturbations Brains have evolved to do more with less. Take a tiny insect brain, which has less than a million neurons but shows a diversity of behaviors and is more energy- efficient than current AI systems. These tiny brains serve as models for computing systems that are becoming more sophisticated as billions of silicon neurons can be implemented on hardware. The secret to achieving energy-efficiency lies in the silicon neurons ability to learn to communicate and form networks, as shown by new research from the lab of Shantanu Chakrabartty, the Clifford W. Murphy Professor in the Preston M. Green Department of Electrical & Systems Engineering at Washington University in St. Louis McKelvey School of Engineering.

E lisle hughesCliffordw murphyKevin baconEmily hendersonSrikanth singamaneniBarani ramanShantanu chakrabarttyAhana gangopadhyayLoui mckelvey school of engineeringDepartment of biomedical engineeringDepartment of mechanical engineering materials scienceSystems engineeringWashington universityLoui mckelvey schoolBiomedical engineeringHughes professor