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Neuromorphic Computing

Schuller Data acquisition (sensors) and manipulation (memory, computation, communications, data mining) in its many forms drives and fuels our civilization. Scientific and technological developments in the last 50 years, have led to the invention of highly sophisticated data acquisition and manipulation machines. Since biological systems can, in many cases, outperform artificial systems a natural question arises. Can biology provide, some high level, guiding principles useful for the development of revolutionary, new concepts for the development of artificial, intelligent systems ?

I will describe attempts to answer the US White House Nanotechnology-Inspired Grand Challenge for Future Technology: “Create a new type of computer that can proactively interpret and learn from data, solve unfamiliar problems using what it has learned, and operate with the energy efficiency of the human brain” .

The work was supported as part of the “Quantum Materials for Energy Efficient Neuromorphic Computing” an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science