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Items: 1 to 20 of 122

1.

Information processing capacity of dynamical systems.

Dambre J, Verstraeten D, Schrauwen B, Massar S.

Sci Rep. 2012;2:514. doi: 10.1038/srep00514.

2.

[Dynamic paradigm in psychopathology: "chaos theory", from physics to psychiatry].

Pezard L, Nandrino JL.

Encephale. 2001 May-Jun;27(3):260-8. French.

PMID:
11488256
3.

Real-time computing without stable states: a new framework for neural computation based on perturbations.

Maass W, Natschläger T, Markram H.

Neural Comput. 2002 Nov;14(11):2531-60.

PMID:
12433288
4.

Propagation and control of stochastic signals through universal learning networks.

Hirasawa K, Mabu S, Hu J.

Neural Netw. 2006 May;19(4):487-99.

PMID:
16423502
5.

Developing learning algorithms via optimized discretization of continuous dynamical systems.

Tao Q, Sun Z, Kong K.

IEEE Trans Syst Man Cybern B Cybern. 2012 Feb;42(1):140-9. doi: 10.1109/TSMCB.2011.2163506.

PMID:
21880573
6.

Back-propagation learning of infinite-dimensional dynamical systems.

Tokuda I, Tokunaga R, Aihara K.

Neural Netw. 2003 Oct;16(8):1179-93.

PMID:
13678621
7.

Hybrid internal model control and proportional control of chaotic dynamical systems.

Qi DL, Yao LB.

J Zhejiang Univ Sci. 2004 Jan;5(1):62-7.

PMID:
14663854
8.

Evolution of cellular automata with memory: The Density Classification Task.

Stone C, Bull L.

Biosystems. 2009 Aug;97(2):108-16. doi: 10.1016/j.biosystems.2009.05.001.

PMID:
19442703
9.

Towards computing with proteins.

Unger R, Moult J.

Proteins. 2006 Apr 1;63(1):53-64.

PMID:
16435369
11.

Automated design of complex dynamic systems.

Hermans M, Schrauwen B, Bienstman P, Dambre J.

PLoS One. 2014 Jan 31;9(1):e86696. doi: 10.1371/journal.pone.0086696.

12.

Time-free spiking neural P systems.

Pan L, Zeng X, Zhang X.

Neural Comput. 2011 May;23(5):1320-42. doi: 10.1162/NECO_a_00115.

PMID:
21299423
13.

Prediction and identification using wavelet-based recurrent fuzzy neural networks.

Lin CJ, Chin CC.

IEEE Trans Syst Man Cybern B Cybern. 2004 Oct;34(5):2144-54.

PMID:
15503511
14.

Distributed parallel computing in stochastic modeling of groundwater systems.

Dong Y, Li G, Xu H.

Ground Water. 2013 Mar;51(2):293-7. doi: 10.1111/j.1745-6584.2012.00967.x.

PMID:
22823593
15.

A machine learning evaluation of an artificial immune system.

Glickman M, Balthrop J, Forrest S.

Evol Comput. 2005 Summer;13(2):179-212.

PMID:
15969900
16.

Learning to imitate stochastic time series in a compositional way by chaos.

Namikawa J, Tani J.

Neural Netw. 2010 Jun;23(5):625-38. doi: 10.1016/j.neunet.2009.12.006.

PMID:
20045751
17.

Memory traces in dynamical systems.

Ganguli S, Huh D, Sompolinsky H.

Proc Natl Acad Sci U S A. 2008 Dec 2;105(48):18970-5. doi: 10.1073/pnas.0804451105.

18.

Dynamical synapses enhance neural information processing: gracefulness, accuracy, and mobility.

Fung CC, Wong KY, Wang H, Wu S.

Neural Comput. 2012 May;24(5):1147-85. doi: 10.1162/NECO_a_00269.

PMID:
22295986
19.

Learning the dynamical system behind sensory data.

Lee J, Lee SY.

Neural Comput. 2010 Jun;22(6):1615-45. doi: 10.1162/neco.2010.01-09-941.

PMID:
20100074
20.

Reservoir computing and extreme learning machines for non-linear time-series data analysis.

Butcher JB, Verstraeten D, Schrauwen B, Day CR, Haycock PW.

Neural Netw. 2013 Feb;38:76-89. doi: 10.1016/j.neunet.2012.11.011.

PMID:
23275138
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