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.
Connective issue: AI learns by doing more with less
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Connective issue: AI learns by doing more with less | The Source | Washington University in St Louis
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Scientists Use Nanoparticles as Heaters to Control Electrical Activity of Neurons
Reviewed by Laura ThomsonJul 22 2021
Nanomaterials have been used in a variety of emerging applications, such as in targeted pharmaceuticals or to bolster other materials and products such as sensors and energy harvesting and storage devices. A team in the McKelvey School of Engineering at Washington University in St. Louis is using nanoparticles as heaters to manipulate the electrical activity of neurons in the brain and of cardiomyocytes in the heart.
The findings, published July 3, 2021, in
Advanced Materials, have the potential to be translated to other types of excitable cells and serve as a valuable tool in nano-neuroengineering.