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The notion of memory capacity, originally introduced for echo state and linear networks with independent inputs, is generalized to nonlinear recurrent networks with stationary but dependent inputs.
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A. Rodan and P. Tino · 2011
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Bai Zhang, D. J. Miller, and Yue Wang · 2012
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“Information processing capacity of dynamical systems”
J. Dambre, D. Verstraeten, B. Schrauwen, and S. Massar · 2012
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“Memory and nonlinear mapping in reservoir computing with two uncoupled nonlinear delay nodes”
S. Ortin, L. Pesquera, and J. M. Gutiérrez · 2012
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“Re-visiting the echo state property.”
I. B. Yildiz, H. Jaeger, and S. J. Kiebel · 2012
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Matrix Analysis
R. A. Horn and C. R. Johnson · 2013
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“Reservoir computing: information processing of stationary signals”
L. Grigoryeva, J. Henriques, and J.-P. Ortega · 2016
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“Determination of the edge of criticality in echo state networks through Fisher information maximization”
L. Livi, F. M. Bianchi, and C. Alippi · 2016
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G. Wainrib and M. N. Galtier · 2016
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P. V. Aceituno, Y. Gang, and Y.-Y. Liu · 2017
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A. S. Charles, D. Yin, and C. J. Rozell · 2017
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C. Gallicchio and A. Micheli · 2017
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“Echo state property linked to an input: exploring a fundamental characteristic of recurrent neural networks”
G. Manjunath and H. Jaeger · 2013
Cited alongside, same era.
“Short term memory in input-driven linear dynamical systems”
P. Tino and A. Rodan · 2013
Cited alongside, same era.
“Memory capacity of input-driven echo state networks at the edge of chaos”
P. Barancok and I. Farkas · 2014
Cited alongside, same era.
“Short term network memory capacity via the restricted isometry property”
A. Charles, H. Yap, and C. Rozell · 2014
Cited alongside, same era.
“Stochastic time series forecasting using time-delay reservoir computers: performance and universality”
L. Grigoryeva, J. Henriques, L. Larger, and J.-P. Ortega · 2014
Cited alongside, same era.
Topology
J. Munkres · 2014
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“Difference between memory and prediction in linear recurrent networks”
S. Marzen · 2017
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“The combination of circle topology and leaky integrator neurons remarkably improves the performance of echo state network on time series prediction.”
F. Xue, Q. Li, and X. Li · 2017
Later among the works it cites.
“Echo state networks are universal”
L. Grigoryeva and J.-P. Ortega · 2018
Later among the works it cites.
“Asymptotic Fisher memory of randomized linear symmetric Echo State Networks”
P. Tino · 2018
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“Risk bounds for reservoir computing”
L. Gonon, L. Grigoryeva, and J.-P. Ortega · 2019
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“Differentiable reservoir computing”
L. Grigoryeva and J.-P. Ortega · 2019
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“Tackling the trade-off between information processing capacity and rate in delay-based reservoir computers”
S. Ortín and L. Pesquera · 2019
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“Echo state networks with self-normalizing activations on the hyper-sphere”
P. Verzelli, C. Alippi, and L. Livi · 2019
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“Delay-based reservoir computing: tackling performance degradation due to system response time”
S. Ortín and L. Pesquera · 2020
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“Dynamical systems as temporal feature spaces”
P. Tino · 2020
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“Input representation in recurrent neural networks dynamics”
P. Verzelli, C. Alippi, L. Livi, and P. Tino · 2020
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