Neuromorphic Adaptive Plastic Scalable Electronics: Analog Learning Systems
| Autor: | N. Srinivasa, J. M. Cruz-Albrecht |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Artificial neural network
business.industry Computer science Transfer Psychology Biomedical Engineering Intelligent decision support system General Medicine Neuromorphic engineering Computer architecture Adaptive system Scalability Animals Humans Neural Networks Computer Electronics State (computer science) Artificial intelligence Architecture business Software |
| Zdroj: | IEEE Pulse. 3:51-56 |
| ISSN: | 2154-2287 |
| DOI: | 10.1109/mpul.2011.2175639 |
| Popis: | Decades of research to build programmable intelligent machines have demonstrated limited utility in complex, real-world environments. Comparing their performance with biological systems, these machines are less efficient by a factor of 1 million1 billion in complex, real-world environments. The Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program is a multifaceted Defense Advanced Research Projects Agency (DARPA) project that seeks to break the programmable machine paradigm and define a new path for creating useful, intelligent machines. Since real-world systems exhibit infinite combinatorial complexity, electronic neuromorphic machine technology would be preferable in a host of applications, but useful and practical implementations still do not exist. HRL Laboratories LLC has embarked on addressing these challenges, and, in this article, we provide an overview of our project and progress made thus far. |
| Databáze: | OpenAIRE |
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