Recent advances in physical reservoir computing: A review
Gouhei Tanaka, Toshiyuki Yamane, J. B. Héroux low, Ryosho Nakane, Naoki Kanazawa, Seiji Takeda, Hidetoshi Numata low, Daiju Nakano low, Akira Hirose
Reservoir computing is a computational framework suited for temporal/sequential data processing. It is derived from several recurrent neural network models, including echo state networks and liquid state machines. A reservoir computing system consists of a reservoir for mapping inputs into a high-dimensional space and a readout for pattern analysis from the high-dimensional states in the reservoir. The reservoir is fixed and only the readout is trained with a simple method such as linear regression and classification. Thus, the major advantage of reservoir computing compared to other recurrent neural networks is fast learning, resulting in low training cost. Another advantage is that the reservoir without adaptive updating is amenable to hardware implementation using a variety of physical systems, substrates, and devices. In fact, such physical reservoir computing has attracted increasing attention in diverse fields of research. The purpose of this review is to provide an overview of recent advances in physical reservoir computing by classifying them according to the type of the reservoir. We discuss the current issues and perspectives related to physical reservoir computing, in order to further expand its practical applications and develop next-generation machine learning systems.
Citation edge evidence
- Recent advances in physical reservoir computing: A review→Long Short-Term MemoryLegacy edge — source detail unavailable
- Recent advances in physical reservoir computing: A review→Deep learning in neural networks: An overviewLegacy edge — source detail unavailable
- Recent advances in physical reservoir computing: A review→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
- Recent advances in physical reservoir computing: A review→Advances in Neural Information Processing Systems 14Legacy edge — source detail unavailable
- Recent advances in physical reservoir computing: A review→Backpropagation through time: what it does and how to do itLegacy edge — source detail unavailable
- Recent advances in physical reservoir computing: A review→A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksLegacy edge — source detail unavailable
- Recent advances in physical reservoir computing: A review→Real-Time Computing Without Stable States: A New Framework for Neural Computation Based o…Legacy edge — source detail unavailable
- Recent advances in physical reservoir computing: A review→Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communi…Legacy edge — source detail unavailable
- Recent advances in physical reservoir computing: A review→Reservoir computing approaches to recurrent neural network trainingLegacy edge — source detail unavailable
- Recent advances in physical reservoir computing: A review→Neural Networks: Tricks of the TradeLegacy edge — source detail unavailable
- Recent advances in physical reservoir computing: A review→The prefrontal cortexLegacy edge — source detail unavailable
- Recent advances in physical reservoir computing: A review→Information processing using a single dynamical node as complex systemLegacy edge — source detail unavailable
- Recent advances in physical reservoir computing: A review→Kernel methods in machine learningLegacy edge — source detail unavailable
- Information processing using a single dynamical node as complex system→Real-Time Computing Without Stable States: A New Framework for Neural Computation Based o…Legacy edge — source detail unavailable
- Information processing using a single dynamical node as complex system→Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communi…Legacy edge — source detail unavailable
- Information processing using a single dynamical node as complex system→Reservoir computing approaches to recurrent neural network trainingLegacy edge — source detail unavailable
- A Learning Algorithm for Continually Running Fully Recurrent Neural Networks→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
- Deep learning in neural networks: An overview→Neural Networks: Tricks of the TradeLegacy edge — source detail unavailable
- Deep learning in neural networks: An overview→A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksLegacy edge — source detail unavailable
- Deep learning in neural networks: An overview→Long Short-Term MemoryLegacy edge — source detail unavailable
- Deep learning in neural networks: An overview→Real-Time Computing Without Stable States: A New Framework for Neural Computation Based o…Legacy edge — source detail unavailable
- Deep learning in neural networks: An overview→Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communi…Legacy edge — source detail unavailable
- Deep learning in neural networks: An overview→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
- Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communi…→A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksLegacy edge — source detail unavailable
- Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communi…→Real-Time Computing Without Stable States: A New Framework for Neural Computation Based o…Legacy edge — source detail unavailable
- Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communi…→Backpropagation through time: what it does and how to do itLegacy edge — source detail unavailable
- Backpropagation through time: what it does and how to do it→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
- Reservoir computing approaches to recurrent neural network training→A Learning Algorithm for Continually Running Fully Recurrent Neural NetworksLegacy edge — source detail unavailable
- Reservoir computing approaches to recurrent neural network training→Long Short-Term MemoryLegacy edge — source detail unavailable
- Reservoir computing approaches to recurrent neural network training→Real-Time Computing Without Stable States: A New Framework for Neural Computation Based o…Legacy edge — source detail unavailable
- Reservoir computing approaches to recurrent neural network training→Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communi…Legacy edge — source detail unavailable
- Reservoir computing approaches to recurrent neural network training→Backpropagation through time: what it does and how to do itLegacy edge — source detail unavailable
- Reservoir computing approaches to recurrent neural network training→Advances in Neural Information Processing Systems 14Legacy edge — source detail unavailable
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Topics
| Neural Networks and Reservoir Computing | Computer Science |
| Advanced Memory and Neural Computing | Engineering |
| Neural dynamics and brain function | Neuroscience |
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