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Correction: Computational capabilities of a multicellular reservoir computing system. [PDF]
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Multicellular Reservoir Computing
2022AbstractThe capacity of cells to process information is currently used to design cell-based tools for ecological, industrial, and biomedical applications such as detecting dangerous chemicals or for bioremediation. In most applications, individual cells are used as the information processing unit.
Vladimir Nikolić +3 more
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Multifunctional reservoir computing
Physical Review EWhereas the power of reservoir computing (RC) in inferring chaotic systems has been well established in the literature, the studies are mostly restricted to monofunctional machines where the training and testing data are acquired from the same attractor.
Yao Du +5 more
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2021
This chapter surveys the recent advancements on the extension of Reservoir Computing toward deep architectures, which is gaining increasing research attention in the neural networks community. Within this context, we focus on describing the major features of Deep Echo State Networks based on the hierarchical composition of multiple reservoirs.
Gallicchio C., Micheli A.
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This chapter surveys the recent advancements on the extension of Reservoir Computing toward deep architectures, which is gaining increasing research attention in the neural networks community. Within this context, we focus on describing the major features of Deep Echo State Networks based on the hierarchical composition of multiple reservoirs.
Gallicchio C., Micheli A.
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KI - Künstliche Intelligenz, 2012
Reservoir Computing (RC) is a paradigm of understanding and training Recurrent Neural Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the readouts from it. It started ten years ago and is currently a prolific research area, giving important insights into RNNs, practical machine learning tools, as well as enabling ...
Mantas Lukoševičius +2 more
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Reservoir Computing (RC) is a paradigm of understanding and training Recurrent Neural Networks (RNNs) based on treating the recurrent part (the reservoir) differently than the readouts from it. It started ten years ago and is currently a prolific research area, giving important insights into RNNs, practical machine learning tools, as well as enabling ...
Mantas Lukoševičius +2 more
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Consistency Hierarchy of Reservoir Computers
IEEE Transactions on Neural Networks and Learning Systems, 2022We study the propagation and distribution of information-carrying signals injected in dynamical systems serving as reservoir computers. Through different combinations of repeated input signals, a multivariate correlation analysis reveals measures known as the consistency spectrum and consistency capacity.
Thomas Jungling +2 more
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2008 Second UKSIM European Symposium on Computer Modeling and Simulation, 2008
In trying to mimic biological functions of the brain, artificial neural network (ANN) research has, out of computational necessity, made a number of assumptions. Firstly, it is assumed that the complexity of biological processes can be usefully replicated artificially by abstracting a relatively few key or essential characteristics from the biological ...
David Reid, Mark Barrett-Baxendale
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In trying to mimic biological functions of the brain, artificial neural network (ANN) research has, out of computational necessity, made a number of assumptions. Firstly, it is assumed that the complexity of biological processes can be usefully replicated artificially by abstracting a relatively few key or essential characteristics from the biological ...
David Reid, Mark Barrett-Baxendale
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Reservoir Computing with Computational Matter
2018The reservoir computing paradigm of information processing has emerged as a natural response to the problem of training recurrent neural networks. It has been realized that the training phase can be avoided provided a network has some well-defined properties, e.g. the echo state property. This idea has been generalized to arbitrary artificial dynamical
Zoran Konkoli +3 more
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Memcapacitive reservoir computing
2017 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH), 2017Memristors have successfully been used to build efficient reservoir computers. The power consumption of memristive reservoirs, however, is bounded by the resistive nature of such devices. Here, we show that memcapacitors, another device in the mem-device family, offer great promise for power-efficient reservoir computers.
Tran, Dat, Teuscher, Christof
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