AI Bibliography
AI Bibliography |
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Nakajima, K. (2020). Physical reservoir computing—an introductory perspective. Japanese Journal of Applied Physics, 59(6), 60501. |
| Resource type: Journal Article BibTeX citation key: Nakajima2020 View all bibliographic details  |
Categories: Artificial Intelligence, Biological Science, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, Engineering, General, Geopolitical, Neuroscience Subcategories: Autonomous systems, Chaos theory, Decision making, Deep learning, Edge AI, Japan, Machine intelligence, Machine learning, Neural nets, Neurosymbolic, Robotics, Synthetic intelligence Creators: Nakajima Publisher: Collection: Japanese Journal of Applied Physics |
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| Abstract |
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Understanding the fundamental relationships between physics and its information-processing capability has been an active research topic for many years. Physical reservoir computing is a recently introduced framework that allows one to exploit the complex dynamics of physical systems as information-processing devices. This framework is particularly suited for edge computing devices, in which information processing is incorporated at the edge (e.g. into sensors) in a decentralized manner to reduce the adaptation delay caused by data transmission overhead. This paper aims to illustrate the potentials of the framework using examples from soft robotics and to provide a concise overview focusing on the basic motivations for introducing it, which stem from a number of fields, including machine learning, nonlinear dynamical systems, biological science, materials science, and physics.
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