메뉴 건너뛰기




Volumn 26, Issue 4, 2012, Pages 365-371

Reservoir Computing Trends

Author keywords

Echo state network; Recurrent neural network; Reservoir computing

Indexed keywords

'CURRENT; BASIC CONCEPTS; ECHO STATE NETWORKS; NETWORK-BASED; RESEARCH AREAS; RESERVOIR COMPUTING;

EID: 85087935180     PISSN: 09331875     EISSN: 16101987     Source Type: Journal    
DOI: 10.1007/s13218-012-0204-5     Document Type: Article
Times cited : (366)

References (53)
  • 1
    • 0034186923 scopus 로고    scopus 로고
    • New results on recurrent network training: unifying the algorithms and accelerating convergence
    • Atiya AF, Parlos AG (2000) New results on recurrent network training: unifying the algorithms and accelerating convergence. IEEE Trans Neural Netw 11(3):697–709 DOI: 10.1109/72.846741
    • (2000) IEEE Trans Neural Netw , vol.11 , Issue.3 , pp. 697-709
    • Atiya, A.F.1    Parlos, A.G.2
  • 2
    • 0028392483 scopus 로고
    • Learning long-term dependencies with gradient descent is difficult
    • Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166 DOI: 10.1109/72.279181
    • (1994) IEEE Trans Neural Netw , vol.5 , Issue.2 , pp. 157-166
    • Bengio, Y.1    Simard, P.2    Frasconi, P.3
  • 3
    • 79952006391 scopus 로고    scopus 로고
    • A reservoir of time constants for memory traces in cortical neurons
    • Bernacchia A, Seo H, Lee D, Wang XJ (2011) A reservoir of time constants for memory traces in cortical neurons. Nat Neurosci 14(3):366–372 DOI: 10.1038/nn.2752
    • (2011) Nat Neurosci , vol.14 , Issue.3 , pp. 366-372
    • Bernacchia, A.1    Seo, H.2    Lee, D.3    Wang, X.J.4
  • 4
    • 81355133300 scopus 로고    scopus 로고
    • Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons
    • Buesing L, Bill J, Nessler B, Maass W (2011) Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons. PLoS Comput Biol 7(11):e1002211 DOI: 10.1371/journal.pcbi.1002211
    • (2011) PLoS Comput Biol , vol.7 , Issue.11
    • Buesing, L.1    Bill, J.2    Nessler, B.3    Maass, W.4
  • 6
    • 58549091090 scopus 로고    scopus 로고
    • State-dependent computations: spatiotemporal processing in cortical networks
    • Buonomano DV, Maass W (2009) State-dependent computations: spatiotemporal processing in cortical networks. Nat Rev, Neurosci 10(2):113–125. http://www.ncbi.nlm.nih.gov/pubmed/19145235 DOI: 10.1038/nrn2558
    • (2009) Nat Rev, Neurosci , vol.10 , Issue.2 , pp. 113-125
    • Buonomano, D.V.1    Maass, W.2
  • 7
    • 80054023232 scopus 로고    scopus 로고
    • Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing
    • Buteneers P, Verstraeten D, van Mierlo P, Wyckhuys T, Stroobandt D, Raedt R, Hallez H, Schrauwen B (2011) Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing. Artif Intell Med 53(3):215–223 DOI: 10.1016/j.artmed.2011.08.006
    • (2011) Artif Intell Med , vol.53 , Issue.3 , pp. 215-223
    • Buteneers, P.1    Verstraeten, D.2    van Mierlo, P.3    Wyckhuys, T.4    Stroobandt, D.5    Raedt, R.6    Hallez, H.7    Schrauwen, B.8
  • 8
    • 0029352040 scopus 로고
    • Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning
    • Dominey PF (1995) Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning. Biol Cybern 73:265–274 DOI: 10.1007/BF00201428
    • (1995) Biol Cybern , vol.73 , pp. 265-274
    • Dominey, P.F.1
  • 9
    • 29344465148 scopus 로고    scopus 로고
    • From sensorimotor sequence to grammatical construction: evidence from simulation and neurophysiology
    • Dominey PF (2005) From sensorimotor sequence to grammatical construction: evidence from simulation and neurophysiology. Adapt Behav 13(4):347–361 DOI: 10.1177/105971230501300401
    • (2005) Adapt Behav , vol.13 , Issue.4 , pp. 347-361
    • Dominey, P.F.1
  • 10
    • 0034120139 scopus 로고    scopus 로고
    • Neural network processing of natural language. I. Sensitivity to serial, temporal and abstract structure of language in the infant
    • Dominey PF, Ramus F (2000) Neural network processing of natural language. I. Sensitivity to serial, temporal and abstract structure of language in the infant. Lang Cogn Processes 15(1):87–127 DOI: 10.1080/016909600386129
    • (2000) Lang Cogn Processes , vol.15 , Issue.1 , pp. 87-127
    • Dominey, P.F.1    Ramus, F.2
  • 13
    • 83455164028 scopus 로고    scopus 로고
    • Recurrent kernel machines: computing with infinite echo state networks
    • Hermans M, Schrauwen B (2012) Recurrent kernel machines: computing with infinite echo state networks. Neural Comput 24(1):104–133. doi:10.1162/NECO_a_00200 DOI: 10.1162/NECO_a_00200
    • (2012) Neural Comput , vol.24 , Issue.1 , pp. 104-133
    • Hermans, M.1    Schrauwen, B.2
  • 14
    • 82555165808 scopus 로고    scopus 로고
    • A three-layered model of primate prefrontal cortex encodes identity and abstract categorical structure of behavioral sequences
    • Hinaut X, Dominey PF (2011) A three-layered model of primate prefrontal cortex encodes identity and abstract categorical structure of behavioral sequences. J Physiol 105(1–3):16–24
    • (2011) J Physiol , vol.105 , Issue.1-3 , pp. 16-24
    • Hinaut, X.1    Dominey, P.F.2
  • 15
    • 0031573117 scopus 로고    scopus 로고
    • Long short-term memory
    • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780 DOI: 10.1162/neco.1997.9.8.1735
    • (1997) Neural Comput , vol.9 , Issue.8 , pp. 1735-1780
    • Hochreiter, S.1    Schmidhuber, J.2
  • 16
    • 40049092971 scopus 로고    scopus 로고
    • Central pattern generators for locomotion control in animals and robots: a review
    • Ijspeert AJ (2008) Central pattern generators for locomotion control in animals and robots: a review. Neural Netw 21:642–653 DOI: 10.1016/j.neunet.2008.03.014
    • (2008) Neural Netw , vol.21 , pp. 642-653
    • Ijspeert, A.J.1
  • 19
    • 1842436050 scopus 로고    scopus 로고
    • The “echo state” approach to analysing and training recurrent neural networks
    • Tech Rep GMD report 148
    • Jaeger H (2001) The “echo state” approach to analysing and training recurrent neural networks. Tech Rep GMD report 148, German National Research Center for Information Technology. http://www.faculty.jacobs-university.de/hjaeger/pubs/EchoStatesTechRep.pdf
    • (2001) German National Research Center for Information Technology
    • Jaeger, H.1
  • 20
    • 33749833931 scopus 로고    scopus 로고
    • Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the echo state network approach
    • Jaeger H (2002) Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the echo state network approach. GMD Report 159, Fraunhofer Institute AIS. http://minds.jacobs-university.de/pubs
    • (2002) GMD Report 159, Fraunhofer Institute AIS.
    • Jaeger, H.1
  • 21
    • 58049158689 scopus 로고    scopus 로고
    • Echo state network
    • Jaeger H (2007) Echo state network. Scholarpedia 2(9):2330. http://www.scholarpedia.org/article/Echo_state_network DOI: 10.4249/scholarpedia.2330
    • (2007) Scholarpedia , vol.2 , Issue.9 , pp. 2330
    • Jaeger, H.1
  • 22
    • 1842421269 scopus 로고    scopus 로고
    • Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication
    • Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667):78–80. doi:10.1126/science.1091277 DOI: 10.1126/science.1091277
    • (2004) Science , vol.304 , Issue.5667 , pp. 78-80
    • Jaeger, H.1    Haas, H.2
  • 23
    • 34249938474 scopus 로고    scopus 로고
    • Optimization and applications of echo state networks with leaky-integrator neurons
    • Jaeger H, Lukoševičius M, Popovici D, Siewert U (2007) Optimization and applications of echo state networks with leaky-integrator neurons. Neural Netw 20(3):335–352 DOI: 10.1016/j.neunet.2007.04.016
    • (2007) Neural Netw , vol.20 , Issue.3 , pp. 335-352
    • Jaeger, H.1    Lukoševičius, M.2    Popovici, D.3    Siewert, U.4
  • 26
    • 84856468111 scopus 로고    scopus 로고
    • Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing
    • Larger L, Soriano MC, Brunner D, Appeltant L, Gutierrez JM, Pesquera L, Mirasso CR, Fischer I (2012) Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. Opt Express 20:3241–3249. doi:10.1364/OE.20.003241 DOI: 10.1364/OE.20.003241
    • (2012) Opt Express , vol.20 , pp. 3241-3249
    • Larger, L.1    Soriano, M.C.2    Brunner, D.3    Appeltant, L.4    Gutierrez, J.M.5    Pesquera, L.6    Mirasso, C.R.7    Fischer, I.8
  • 27
    • 77954098778 scopus 로고    scopus 로고
    • A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task
    • Legenstein R, Chase SM, Schwartz AB, Maass W (2010) A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task. J Neurosci 30(25):8400–8410 DOI: 10.1523/JNEUROSCI.4284-09.2010
    • (2010) J Neurosci , vol.30 , Issue.25 , pp. 8400-8410
    • Legenstein, R.1    Chase, S.M.2    Schwartz, A.B.3    Maass, W.4
  • 30
    • 68649088777 scopus 로고    scopus 로고
    • Reservoir computing approaches to recurrent neural network training
    • Lukoševičius M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training. Comput Sci Rev 3(3):127–149. doi:10.1016/j.cosrev.2009.03.005 DOI: 10.1016/j.cosrev.2009.03.005
    • (2009) Comput Sci Rev , vol.3 , Issue.3 , pp. 127-149
    • Lukoševičius, M.1    Jaeger, H.2
  • 31
    • 68649128418 scopus 로고    scopus 로고
    • Time warping invariant echo state networks
    • International University Bremen
    • Lukoševičius M, Popovici D, Jaeger H, Siewert U (2006) Time warping invariant echo state networks. IUB technical report 2, International University Bremen. http://minds.jacobs-university.de/pubs
    • (2006) IUB Technical Report , vol.2
    • Lukoševičius, M.1    Popovici, D.2    Jaeger, H.3    Siewert, U.4
  • 32
    • 84986578247 scopus 로고    scopus 로고
    • Motivation, theory, and applications of liquid state machines
    • Cooper B, Sorbi A, (eds), Imperial College Press, London
    • Maass W (2011) Motivation, theory, and applications of liquid state machines. In: Cooper B, Sorbi A (eds) Computability in context: computation and logic in the real world. Imperial College Press, London, pp 275–296 DOI: 10.1142/9781848162778_0008
    • (2011) Computability in context: computation and logic in the real world , pp. 275-296
    • Maass, W.1
  • 33
    • 33846556028 scopus 로고    scopus 로고
    • Computational aspects of feedback in neural circuits
    • Maass W, Joshi P, Sontag E (2007) Computational aspects of feedback in neural circuits. PLoS Comput Biol 3(1):1–20 DOI: 10.1371/journal.pcbi.0020165
    • (2007) PLoS Comput Biol , vol.3 , Issue.1 , pp. 1-20
    • Maass, W.1    Joshi, P.2    Sontag, E.3
  • 34
    • 0036834701 scopus 로고    scopus 로고
    • Real-time computing without stable states: a new framework for neural computation based on perturbations
    • Maass W, Natschläger T, Markram H (2002) Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 14(11):2531–2560. doi:10.1162/089976602760407955 DOI: 10.1162/089976602760407955
    • (2002) Neural Comput , vol.14 , Issue.11 , pp. 2531-2560
    • Maass, W.1    Natschläger, T.2    Markram, H.3
  • 35
    • 80053451847 scopus 로고    scopus 로고
    • Learning recurrent neural networks with Hessian-free optimization
    • Martens J, Sutskever I (2011) Learning recurrent neural networks with Hessian-free optimization. In: Proc 28th int conf on machine learning. http://www.icml-2011.org/papers/532_icmlpaper.pdf
    • (2011) Proc 28th int conf on machine learning
    • Martens, J.1    Sutskever, I.2
  • 38
    • 12144261601 scopus 로고    scopus 로고
    • Analyzing the weight dynamics of recurrent learning algorithms
    • Schiller UD, Steil JJ (2005) Analyzing the weight dynamics of recurrent learning algorithms. Neurocomputing 63C:5–23 DOI: 10.1016/j.neucom.2004.04.006
    • (2005) Neurocomputing , vol.63C , pp. 5-23
    • Schiller, U.D.1    Steil, J.J.2
  • 39
    • 40649092298 scopus 로고    scopus 로고
    • Compact hardware liquid state machines on FPGA for real-time speech recognition
    • Schrauwen B, D‘Haene M, Verstraeten D, Stroobandt D (2008) Compact hardware liquid state machines on FPGA for real-time speech recognition. Neural Netw 21(2–3):511–523 DOI: 10.1016/j.neunet.2007.12.009
    • (2008) Neural Netw , vol.21 , Issue.2-3 , pp. 511-523
    • Schrauwen, B.1    D‘Haene, M.2    Verstraeten, D.3    Stroobandt, D.4
  • 41
    • 34047166022 scopus 로고    scopus 로고
    • Support vector echo-state machine for chaotic time-series prediction
    • Shi Z, Han M (2007) Support vector echo-state machine for chaotic time-series prediction. IEEE Trans Neural Netw 18(2):359–372 DOI: 10.1109/TNN.2006.885113
    • (2007) IEEE Trans Neural Netw , vol.18 , Issue.2 , pp. 359-372
    • Shi, Z.1    Han, M.2
  • 42
    • 34249867443 scopus 로고    scopus 로고
    • Automatic speech recognition using a predictive echo state network classifier
    • Skowronski MD, Harris JG (2007) Automatic speech recognition using a predictive echo state network classifier. Neural Netw 20(3):414–423. doi:10.1016/j.neunet.2007.04.006 DOI: 10.1016/j.neunet.2007.04.006
    • (2007) Neural Netw , vol.20 , Issue.3 , pp. 414-423
    • Skowronski, M.D.1    Harris, J.G.2
  • 44
    • 84855432007 scopus 로고    scopus 로고
    • Emergent criticality in complex Turing B-type atomic switch networks
    • Stieg AZ, Avizienis AV, Sillin HO, Martin-Olmos C, Aono M, Gimzewski JK (2012) Emergent criticality in complex Turing B-type atomic switch networks. Adv Mater 24(2):286–293. doi:10.1002/adma.201103053 DOI: 10.1002/adma.201103053
    • (2012) Adv Mater , vol.24 , Issue.2 , pp. 286-293
    • Stieg, A.Z.1    Avizienis, A.V.2    Sillin, H.O.3    Martin-Olmos, C.4    Aono, M.5    Gimzewski, J.K.6
  • 45
    • 68949147577 scopus 로고    scopus 로고
    • Generating coherent patterns of activity from chaotic neural networks
    • Sussillo D, Abbott LF (2009) Generating coherent patterns of activity from chaotic neural networks. Neuron 63(4):544–557. doi:10.1016/j.neuron.2009.07.018 DOI: 10.1016/j.neuron.2009.07.018
    • (2009) Neuron , vol.63 , Issue.4 , pp. 544-557
    • Sussillo, D.1    Abbott, L.F.2
  • 49
    • 34249815487 scopus 로고    scopus 로고
    • An experimental unification of reservoir computing methods
    • Verstraeten D, Schrauwen B, D’Haene M, Stroobandt D (2007) An experimental unification of reservoir computing methods. Neural Netw 20(3):391–403 DOI: 10.1016/j.neunet.2007.04.003
    • (2007) Neural Netw , vol.20 , Issue.3 , pp. 391-403
    • Verstraeten, D.1    Schrauwen, B.2    D’Haene, M.3    Stroobandt, D.4
  • 51
    • 23844557176 scopus 로고    scopus 로고
    • Isolated word recognition with the liquid state machine: a case study
    • Verstraeten D, Schrauwen B, Stroobandt D, Van Campenhout J (2005) Isolated word recognition with the liquid state machine: a case study. Inf Process Lett 95(6):521–528 DOI: 10.1016/j.ipl.2005.05.019
    • (2005) Inf Process Lett , vol.95 , Issue.6 , pp. 521-528
    • Verstraeten, D.1    Schrauwen, B.2    Stroobandt, D.3    Van Campenhout, J.4
  • 52
    • 0025503558 scopus 로고
    • Backpropagation through time: what it does and how to do it
    • Werbos PJ (1990) Backpropagation through time: what it does and how to do it. Proc IEEE 78(10):1550–1560 DOI: 10.1109/5.58337
    • (1990) Proc IEEE , vol.78 , Issue.10 , pp. 1550-1560
    • Werbos, P.J.1
  • 53
    • 0001202594 scopus 로고
    • A learning algorithm for continually running fully recurrent neural networks
    • Williams RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1:270–280 DOI: 10.1162/neco.1989.1.2.270
    • (1989) Neural Comput , vol.1 , pp. 270-280
    • Williams, R.J.1    Zipser, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.