Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning

Steil, Jochen J.

Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning

Steil JJ (2007)
Neural Networks 20(3): 353-364.

Zeitschriftenaufsatz | Veröffentlicht | Englisch

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DOI

https://doi.org/10.1016/j.neunet.2007.04.011

Autor*in

Steil, Jochen J.^UniBi

Einrichtung

Technische Fakultät
Research Institute for Cognition and Robotics

Stichworte

CoR-Lab Publication

Erscheinungsjahr

2007

Zeitschriftentitel

Neural Networks

Band

20

Ausgabe

3

Seite(n)

353-364

ISSN

0893-6080

Page URI

https://pub.uni-bielefeld.de/record/2142131

Zitieren

Steil JJ. Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning. Neural Networks. 2007;20(3):353-364.

Steil, J. J. (2007). Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning. Neural Networks, 20(3), 353-364. https://doi.org/10.1016/j.neunet.2007.04.011

Steil, Jochen J. 2007. “Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning”. Neural Networks 20 (3): 353-364.

Steil, J. J. (2007). Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning. Neural Networks 20, 353-364.

Steil, J.J., 2007. Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning. Neural Networks, 20(3), p 353-364.

J.J. Steil, “Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning”, Neural Networks, vol. 20, 2007, pp. 353-364.

Steil, J.J.: Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning. Neural Networks. 20, 353-364 (2007).

Steil, Jochen J. “Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning”. Neural Networks 20.3 (2007): 353-364.

13 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Training echo state networks for rotation-invariant bone marrow cell classification.
Kainz P, Burgsteiner H, Asslaber M, Ahammer H., Neural Comput Appl 28(6), 2017
PMID: 28706349

Dynamic Neural Fields with Intrinsic Plasticity.
Strub C, Schöner G, Wörgötter F, Sandamirskaya Y., Front Comput Neurosci 11(), 2017
PMID: 28912706

Deep learning in neural networks: an overview.
Schmidhuber J., Neural Netw 61(), 2015
PMID: 25462637

Selection of cortical dynamics for motor behaviour by the basal ganglia.
Mannella F, Baldassarre G., Biol Cybern 109(6), 2015
PMID: 26537483

Computational modeling of neural plasticity for self-organization of neural networks.
Chrol-Cannon J, Jin Y., Biosystems 125(), 2014
PMID: 24769242

Synergies between intrinsic and synaptic plasticity based on information theoretic learning.
Li Y, Li C., PLoS One 8(5), 2013
PMID: 23671642

Effects of cellular homeostatic intrinsic plasticity on dynamical and computational properties of biological recurrent neural networks.
Naudé J, Cessac B, Berry H, Delord B., J Neurosci 33(38), 2013
PMID: 24048833

Simple deterministically constructed cycle reservoirs with regular jumps.
Rodan A, Tiňo P., Neural Comput 24(7), 2012
PMID: 22428595

Minimum complexity echo state network.
Rodan A, Tino P., IEEE Trans Neural Netw 22(1), 2011
PMID: 21075721

Model learning for robot control: a survey.
Nguyen-Tuong D, Peters J., Cogn Process 12(4), 2011
PMID: 21487784

Functional identification of biological neural networks using reservoir adaptation for point processes.
Gürel T, Rotter S, Egert U., J Comput Neurosci 29(1-2), 2010
PMID: 19639401

Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons.
Büsing L, Schrauwen B, Legenstein R., Neural Comput 22(5), 2010
PMID: 20028227

Initialization and self-organized optimization of recurrent neural network connectivity.
Boedecker J, Obst O, Mayer NM, Asada M., HFSP J 3(5), 2009
PMID: 20357891

32 References

Daten bereitgestellt von Europe PubMed Central.

New results on recurrent network training: unifying the algorithms and accelerating convergence.
Atiya AF, Parlos AG., IEEE Trans Neural Netw 11(3), 2000
PMID: 18249797

Responses of neurons in primary and inferior temporal visual cortices to natural scenes.
Baddeley R, Abbott LF, Booth MC, Sengpiel F, Freeman T, Wakeman EA, Rolls ET., Proc. Biol. Sci. 264(1389), 1997
PMID: 9447735

Real-time computation at the edge of chaos in recurrent neural networks.
Bertschinger N, Natschlager T., Neural Comput 16(7), 2004
PMID: 15165396

Long-term plasticity of intrinsic excitability: learning rules and mechanisms.
Daoudal G, Debanne D., Learn. Mem. 10(6), 2003
PMID: 14657257

Plasticity in single neuron and circuit computations.
Destexhe A, Marder E., Nature 431(7010), 2004
PMID: 15483600

Short term memory and pattern matching with simple echo state networks
Fette, 2005

Recurrent neural networks with small weights implement definite memory machines
Hammer, Neural Computation 15(8), 2003

AUTHOR UNKNOWN, 0

Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication.
Jaeger H, Haas H., Science 304(5667), 2004
PMID: 15064413

Movement generation with circuits of spiking neurons.
Joshi P, Maass W., Neural Comput 17(8), 2005
PMID: 15969915

Principles of real-time computing with feedback applied to cortical microcircuit models
Maass, 2006

Real-time computing without stable states: a new framework for neural computation based on perturbations.
Maass W, Natschlager T, Markram H., Neural Comput 14(11), 2002
PMID: 12433288

At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks
Natschläger, 2005

Dynamics of information and emergent computation in generic neural microcircuit models.
Natschlager T, Maass W., Neural Netw 18(10), 2005
PMID: 16150570

The primate cortico-cerebellar system: anatomy and function.
Ramnani N., Nat. Rev. Neurosci. 7(7), 2006
PMID: 16791141

AUTHOR UNKNOWN, 0

Analyzing the weight dynamics of recurrent learning algorithms
Schiller, Neurocomputing 63C(), 2005

AUTHOR UNKNOWN, 0

Backpropagation–decorrelation: Recurrent learning with O(N) complexity
Steil, 2004

AUTHOR UNKNOWN, 0

Online stability of backpropagation–decorrelation recurrent learning
Steil, Neurocomputing 69(7–9), 2006

How voltage-dependent conductances can adapt to maximize the information encoded by neuronal firing rate.
Stemmler M, Koch C., Nat. Neurosci. 2(6), 1999
PMID: 10448216

AUTHOR UNKNOWN, 0

Synergies between intrinsic and synaptic plasticity in individual model neurons
Triesch, 2005

AUTHOR UNKNOWN, 0

Isolated word recognition with the liquid state machine: A case study
Verstraeten, Information Processing Letters 95(6), 2005

AUTHOR UNKNOWN, 0

Sufficient and necessary conditions for global exponential stability of discrete-time recurrent neural networks
Wang, Transactions on Circuits and Systems I 53(6), 2006

AUTHOR UNKNOWN, 0

The other side of the engram: experience-driven changes in neuronal intrinsic excitability.
Zhang W, Linden DJ., Nat. Rev. Neurosci. 4(11), 2003
PMID: 14595400

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