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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 C. 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 C. Reid, Mark Barrett-Baxendale
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Neuromorphic reservoir computing
Chaos: An Interdisciplinary Journal of Nonlinear ScienceReservoir computing has emerged as a promising machine-learning approach to prediction and control of complex nonlinear dynamical systems, rendering it important to explore schemes of physical realization. We articulate two frameworks of physical reservoir computing based on the electrophysiological mechanisms in mammalian neuronal networks.
Shirin Panahi +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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Reservoir Computing with an Ensemble of Time-Delay Reservoirs
Cognitive Computation, 2017Reservoir computing (RC) has attracted a lot of attention in the field of machine learning because of its promising performance in a broad range of applications. However, it is difficult to implement standard RC in hardware. Reservoir computers with a single nonlinear neuron subject to delayed feedback (delay-based RC) allow efficient hardware ...
Silvia Ortin, Luis Pesquera
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Reservoir Computing with an Inertial Form
SIAM Journal on Applied Dynamical Systems, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A reservoir computing approach for molecular computing
The 2018 Conference on Artificial Life, 2018In this paper, we apply the Polymerase-Exonuclease-Nickase Dynamic Network Assembly (PEN DNA) toolbox, a modular framework for molecular computing, to reservoir computing.
Wataru Yahiro +2 more
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Frontiers in Reservoir Computing.
2020Reservoir computing (RC) studies the properties of large recurrent networks of artificial neurons, with either fixed or random connectivity. Over the last years, reservoirs have become a key tool for pattern recognition and neuroscience problems, being able to develop a rich representation of the temporal information even if left untrained.
Claudio Gallicchio +2 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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Reservoir computing on manifolds
Chaos: An Interdisciplinary Journal of Nonlinear ScienceReservoir computing has attracted considerable attention as an effective method for learning chaotic time series generated by dynamical systems. In this paper, we propose a new reservoir computing approach that is adapted to dynamical systems on general manifolds, representing a natural extension of the usual method for dynamical systems on the ...
Masato Hara, Hiroshi Kokubu
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