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- W2595890605 abstract "Neuromorphic computing has become an important emerging research area in recent years. By emulating computational principles and architecture found in neural systems, neuromorphic computing has led to the development of neuromorphic sensors, processors, and sensory motor systems for robotic agents. It has also led to rapid progress in related areas covering computational theories of sensory coding, synaptic computing, learning, and signal processing algorithms, circuit designs, and implementations. The work in these areas shows neuromorphic approaches with appealing computational advantages over conventional approaches, but at the same time, neuromorphic systems still pose many research challenges. Neuromorphic computing overlaps with another area called cyborg intelligence which is dedicated to integrating artificial intelligence (AI) with biological intelligence closely and deeply by connecting computer systems and biological beings. Cyborg intelligence aims to compensate for the weaknesses of both systems by combining the computational power of machines with the perceptive and cognitive abilities of biological systems. Recently, many of the advances in cyborg intelligence methods, systems, and applications have demonstrated the trend of the rapid integration of cyborg intelligence with neuromorphic computing in both breadth and depth. These areas pose innumerable interesting and significant questions for AI and could fundamentally change the landscape of AI research." @default.
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- W2595890605 date "2017-04-01" @default.
- W2595890605 modified "2023-10-03" @default.
- W2595890605 title "Guest Editorial Learning in Neuromorphic Systems and Cyborg Intelligence" @default.
- W2595890605 doi "https://doi.org/10.1109/tnnls.2017.2650599" @default.
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