메뉴 건너뛰기




Volumn 24, Issue 7, 2012, Pages 1822-1852

Simple deterministically constructed cycle reservoirs with regular jumps

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGICAL MODEL; COMPUTER SIMULATION; FEMALE; HUMAN; PHYSIOLOGY; SPEECH PERCEPTION;

EID: 84868229207     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00297     Document Type: Article
Times cited : (123)

References (48)
  • 1
    • 51749120550 scopus 로고    scopus 로고
    • Reservoir optimization in recurrent neural networks using kronecker kernels. In Proceedings of the IEEE Symposium on Circuits and Systems
    • Piscataway, NJ: IEEE
    • Ajdari Rad, A., Jalili, M., and Hasler, M. (2008). Reservoir optimization in recurrent neural networks using kronecker kernels. In Proceedings of the IEEE Symposium on Circuits and Systems (pp. 868-871). Piscataway, NJ: IEEE.
    • (2008) , pp. 868-871
    • Ajdari Rad, A.1    Jalili, M.2    Hasler, M.3
  • 2
    • 0034186923 scopus 로고    scopus 로고
    • New results on recurrent network training:
    • Atiya, A., and Parlos, A. (2000). New results on recurrent network training: Unifying the algorithms and accelerating convergence. IEEE Transactions on Neural.
    • (2000) Networks , vol.11 , pp. 697-709
    • Atiya, A.1    Parlos, A.2
  • 3
    • 2942552269 scopus 로고    scopus 로고
    • Real-time computation at the edge of chaos in recurrent neural networks
    • Bertschinger, N., and Natschlager, T. (2004). Real-time computation at the edge of chaos in recurrent neural networks. Neural Computation, 16(7), 1413-1436.
    • (2004) Neural Computation , vol.16 , Issue.7 , pp. 1413-1436
    • Bertschinger, N.1    Natschlager, T.2
  • 4
    • 84954240138 scopus 로고    scopus 로고
    • Modeling reward functions for incomplete state representations via echo state networks. In Proceedings of the IEEE International Joint Conference on Neural Networks
    • Piscataway, NJ: IEEE.
    • Bush, K., and Anderson, C. (2005). Modeling reward functions for incomplete state representations via echo state networks. In Proceedings of the IEEE International Joint Conference on Neural Networks (pp. 2995-3000). Piscataway, NJ: IEEE.
    • (2005) , pp. 2995-3000
    • Bush, K.1    Anderson, C.2
  • 5
    • 77953355233 scopus 로고    scopus 로고
    • Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons
    • Busing, L., Schrauwen, B., and Legenstein, R. A. (2010). Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons. Neural Computation, 22(5), 1272-1311.
    • (2010) Neural Computation , vol.22 , Issue.5 , pp. 1272-1311
    • Busing, L.1    Schrauwen, B.2    Legenstein, R.A.3
  • 6
    • 33750137073 scopus 로고    scopus 로고
    • Feed-forward echo state networks. In Proceedings of the IEEE International Joint Conference on Neural Networks
    • Piscataway, NJ: IEEE
    • Cernansky, M., and Makula, M. (2005). Feed-forward echo state networks. In Proceedings of the IEEE International Joint Conference on Neural Networks (pp. 1479-1482). Piscataway, NJ: IEEE.
    • (2005) , pp. 1479-1482
    • Cernansky, M.1    Makula, M.2
  • 7
    • 58849107261 scopus 로고    scopus 로고
    • Predictive modelling with echo state networks. In Proceedings of the 18th International Conference on Artificial Neural Networks
    • New York: Springer-Verlag
    • Cernansky, M., and Tino, P. (2008). Predictive modelling with echo state networks. In Proceedings of the 18th International Conference on Artificial Neural Networks (pp. 778-787). New York: Springer-Verlag.
    • (2008) , pp. 778-787
    • Cernansky, M.1    Tino, P.2
  • 8
    • 34548609011 scopus 로고    scopus 로고
    • Collective behavior of a small-world recurrent neural system with scale-free distribution
    • Deng, Z., and Zhang, Y. (2007). Collective behavior of a small-world recurrent neural system with scale-free distribution. IEEE Transactions on Neural Networks, 18(5), 1364-1375.
    • (2007) IEEE Transactions on Neural Networks , vol.18 , Issue.5 , pp. 1364-1375
    • Deng, Z.1    Zhang, Y.2
  • 9
    • 58149236632 scopus 로고    scopus 로고
    • Liquid state machines and cultured cortical networks: The separation property
    • Dockendorf, K., Park, I., Ping, H., Principe, J. C., and DeMarse, T. (2009). Liquid state machines and cultured cortical networks: The separation property. Biosystems, 95(2), 90-97.
    • (2009) Biosystems , vol.95 , Issue.2 , pp. 90-97
    • Dockendorf, K.1    Park, I.2    Ping, H.3    Principe, J.C.4    DeMarse, T.5
  • 11
    • 33646172134 scopus 로고    scopus 로고
    • Short term memory and pattern matching with simple echo state networks. In Proc. of the 15th International Conference on Artificial Neural Networks
    • New York: Springer-Verlag.
    • Fette, G., and Eggert, J. (2005). Short term memory and pattern matching with simple echo state networks. In Proc. of the 15th International Conference on Artificial Neural Networks (pp. 13-18). New York: Springer-Verlag.
    • (2005) , pp. 13-18
    • Fette, G.1    Eggert, J.2
  • 12
    • 6344249564 scopus 로고    scopus 로고
    • Perspectives of the high-dimensional dynamics of neural microcircuits from the point of view of low-dimensional readouts
    • Hausler, S.,Markram,M., andMaass,W. (2003). Perspectives of the high-dimensional dynamics of neural microcircuits from the point of view of low-dimensional readouts. Complexity, 8(4), 39-50.
    • (2003) Complexity , vol.8 , Issue.4 , pp. 39-50
    • Hausler, S.1    Markram, M.2    Maass, W.3
  • 13
    • 73949157176 scopus 로고    scopus 로고
    • Echo state networks with filter neurons and a delay and sum readout
    • Holzmann, G., and Hauser, H. (2009). Echo state networks with filter neurons and a delay and sum readout. Neural Networks, 32(2), 244-256.
    • (2009) Neural Networks , vol.32 , Issue.2 , pp. 244-256
    • Holzmann, G.1    Hauser, H.2
  • 14
    • 19644372764 scopus 로고    scopus 로고
    • Identification of motion with echo state network. In Proceedings of the OCEANS 2004 MTS/IEEE-TECHNOOCEAN Conference
    • Ishii, K., van der Zant, T., Becanovic, V. and Ploger, P. (2004). Identification of motion with echo state network. In Proceedings of the OCEANS 2004 MTS/IEEE-TECHNOOCEAN Conference (Vol. 3, pp. 1205-1210).
    • (2004) , vol.3 , pp. 1205-1210
    • Ishii, K.1    van der Zant, T.2    Becanovic, V.3    Ploger, P.4
  • 15
    • 84874066973 scopus 로고    scopus 로고
    • The "echo state" approach to analysing and training recurrent neural networks (Tech. Rep. No. 148). Sankt Augustin: GermanNational Research Center for Information Technology.
    • Jaeger, H. (2001). The "echo state" approach to analysing and training recurrent neural networks (Tech. Rep. No. 148). Sankt Augustin: GermanNational Research Center for Information Technology.
    • (2001)
    • Jaeger, H.1
  • 16
    • 1842488370 scopus 로고    scopus 로고
    • (Tech. Rep. No. 152). Sankt Augustin: German National Research Center for Information Technology
    • Jaeger,H. (2002a). Short term memory in echo state networks (Tech. Rep. No. 152). Sankt Augustin: German National Research Center for Information Technology.
    • (2002) Short term memory in echo state networks
    • Jaeger, H.1
  • 17
    • 33749833931 scopus 로고    scopus 로고
    • Atutorial on training recurrent neural networks, covering BPPT,RTRL, EKF and the "echo state network" approach
    • (Tech. Rep. No. 159). Sankt Augustin: German National Research Center for Information Technology
    • Jaeger,H. (2002b). Atutorial on training recurrent neural networks, covering BPPT,RTRL, EKF and the "echo state network" approach (Tech. Rep. No. 159). Sankt Augustin: German National Research Center for Information Technology.
    • (2002)
    • Jaeger, H.1
  • 18
    • 78349289898 scopus 로고    scopus 로고
    • S. Becker, S. Thrün, and K. Obermayer (Eds.), Advances in neural information processing systems, 15. Cambridge, MA: MIT Press
    • Jaeger,H. (2003). Adaptive nonlinear systems identification with echo state network. In S. Becker, S. Thrün, and K. Obermayer (Eds.), Advances in neural information processing systems, 15 (pp. 593-600). Cambridge, MA: MIT Press.
    • (2003) Adaptive nonlinear systems identification with echo state network , pp. 593-600
    • Jaeger, H.1
  • 19
    • 33750099080 scopus 로고    scopus 로고
    • Reservoir riddles: Suggestions for echo state network research. In Proceedings of the IEEE International Joint Conference on Neural Networks
    • New York: Springer-Verlag
    • Jaeger, H. (2005). Reservoir riddles: Suggestions for echo state network research. In Proceedings of the IEEE International Joint Conference on Neural Networks (pp. 1460-1462). New York: Springer-Verlag.
    • (2005) , pp. 1460-1462
    • Jaeger, H.1
  • 20
    • 85039661830 scopus 로고    scopus 로고
    • Discovering multiscale dynamical features with hierarchical echo state networks (Tech. Rep. No. 10). Bremen: Jacobs University.
    • Jaeger, H. (2007). Discovering multiscale dynamical features with hierarchical echo state networks (Tech. Rep. No. 10). Bremen: Jacobs University.
    • (2007)
    • Jaeger, H.1
  • 21
    • 1842421269 scopus 로고    scopus 로고
    • Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless telecommunication
    • Jaeger, H., and Hass, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless telecommunication. Science, 304, 78-80.
    • (2004) Science , vol.304 , pp. 78-80
    • Jaeger, H.1    Hass, H.2
  • 22
    • 34249938474 scopus 로고    scopus 로고
    • Optimisation and applications of echo state networks with leaky-integrator neurons
    • Jaeger,H., Lukosevicius,M., Popovici,D.,andSiewert, U. (2007). Optimisation and applications of echo state networks with leaky-integrator neurons. Neural Networks, 20(3), 335-352.
    • (2007) Neural Networks , vol.20 , Issue.3 , pp. 335-352
    • Jaeger, H.1    Lukosevicius, M.2    Popovici, D.3    Siewert, U.4
  • 23
    • 34548726030 scopus 로고    scopus 로고
    • Is there a liquid state machine in the bacterium Escherichia coli? In Proceedings of the 2007 IEEE Symposium on Artificial Life
    • Piscataway, NJ: IEEE.
    • Jones, B., Stekel, D., Rowe, J., and Fernando, C. (2007). Is there a liquid state machine in the bacterium Escherichia coli? In Proceedings of the 2007 IEEE Symposium on Artificial Life (pp. 187-191). Piscataway, NJ: IEEE.
    • (2007) , pp. 187-191
    • Jones, B.1    Stekel, D.2    Rowe, J.3    Fernando, C.4
  • 24
    • 33846543881 scopus 로고    scopus 로고
    • Edge of chaos and prediction of computational performance for neural circuit models
    • Legenstein, R., and Maass, W. (2007). Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks, 20(3), 323-334.
    • (2007) Neural Networks , vol.20 , Issue.3 , pp. 323-334
    • Legenstein, R.1    Maass, W.2
  • 25
    • 68649088777 scopus 로고    scopus 로고
    • Reservoir computing approaches to recurrent neural network training
    • Lukosevicius,M., and Jaeger, H. (2009). Reservoir computing approaches to recurrent neural network training. Computer Science Review, 3(3), 127-149.
    • (2009) Computer Science Review , vol.3 , Issue.3 , pp. 127-149
    • Lukosevicius, M.1    Jaeger, H.2
  • 26
    • 79251542316 scopus 로고
    • A computational model of filtering, detection and compression in the cochlea. In Proceedings of the IEEE ICASSP
    • Piscataway, NJ: IEEE.
    • Lyon, R. F. (1982). A computational model of filtering, detection and compression in the cochlea. In Proceedings of the IEEE ICASSP (pp. 1282-1285). Piscataway, NJ: IEEE. Maass, W., Natschlager, T., and Markram, H. (2002). Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14(11), 2531-2560.
    • (1982) , pp. 1282-1285
    • Lyon, R.F.1
  • 27
    • 0036834701 scopus 로고    scopus 로고
    • Real-time computing without stable states: A new framework for neural computation based on perturbations
    • Maass, W., Natschlager, T., and Markram, H. (2002). Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14(11), 2531-2560.
    • (2002) Neural Computation , vol.14 , Issue.11 , pp. 2531-2560
    • Maass, W.1    Natschlager, T.2    Markram, H.3
  • 28
    • 28644432156 scopus 로고    scopus 로고
    • Fading memory and kernel properties of generic cortical microcircuit models
    • Maass,W., Natschlager, T., andMarkram, H. (2004). Fading memory and kernel properties of generic cortical microcircuit models. Journal of Physiology, 98(4-6), 315-330.
    • (2004) Journal of Physiology , vol.98 , Issue.4-6 , pp. 315-330
    • Maass, W.1    Natschlager, T.2    Markram, H.3
  • 29
    • 33846023013 scopus 로고    scopus 로고
    • Analysis and design of echo state network
    • Ozturk, M. C., Xu, D., and Principe, J. C. (2007). Analysis and design of echo state network. Neural Computation, 19(1), 111-138. Rodan, A., and Ti? no, P. (2011). Minimum complexity echo state network. IEEE Transactions on Neural Networks, 22(1), 131-144.
    • (2007) Neural Computation , vol.19 , Issue.1 , pp. 111-138
    • Ozturk, M.C.1    Xu, D.2    Principe, J.C.3
  • 30
  • 33
    • 38149062901 scopus 로고    scopus 로고
    • The introduction of time-scales in reservoir computing, applied to isolated digits recognition. In Proceedings of the 17th International Conference on Artificial Neural Networks
    • New York: Springer-Verlag.
    • Schrauwen, B., Defour, J., Verstraeten, D., and Van Campenhout, J. M. (2007). The introduction of time-scales in reservoir computing, applied to isolated digits recognition. In Proceedings of the 17th International Conference on Artificial Neural Networks (pp. 471-479). New York: Springer-Verlag.
    • (2007) , pp. 471-479
    • Schrauwen, B.1    Defour, J.2    Verstraeten, D.3    Van Campenhout, J.M.4
  • 35
    • 34249870193 scopus 로고    scopus 로고
    • Minimum mean squared error time series classification using an echo state network prediction model. In IEEE International Symposium on Circuits and Systems
    • Piscataway, NJ: IEEE.
    • Skowronski, M. D., and Harris, J. G. (2006). Minimum mean squared error time series classification using an echo state network prediction model. In IEEE International Symposium on Circuits and Systems (pp. 3153-3156). Piscataway, NJ: IEEE.
    • (2006) , pp. 3153-3156
    • Skowronski, M.D.1    Harris, J.G.2
  • 36
    • 10944225085 scopus 로고    scopus 로고
    • Backpropagation-decorrelation: Recurrent learning with o(n) complexity. In Proc. of the International Joint Conference on Neural Networks
    • New York: Springer-Verlag.
    • Steil, J. (2004). Backpropagation-decorrelation: Recurrent learning with o(n) complexity. In Proc. of the International Joint Conference on Neural Networks (pp. 843-848). New York: Springer-Verlag.
    • (2004) , pp. 843-848
    • Steil, J.1
  • 37
    • 34249811184 scopus 로고    scopus 로고
    • Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning
    • Steil, J. (2007). Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning. Neural Networks, 20, 353-364.
    • (2007) Neural Networks , vol.20 , pp. 353-364
    • Steil, J.1
  • 38
    • 0012940362 scopus 로고    scopus 로고
    • The value of symbolic computation
    • Tabor, W. (2002). The value of symbolic computation. Ecological Psychology, 14(1-2), 21-51.
    • (2002) Ecological Psychology , vol.14 , Issue.1-2 , pp. 21-51
    • Tabor, W.1
  • 39
    • 0000860629 scopus 로고    scopus 로고
    • Predicting the future of discrete sequences from fractal representations of the past
    • Tiňo, P.,andDorffner, G. (2001). Predicting the future of discrete sequences from fractal representations of the past. Machine Learning, 45(2), 187-218.
    • (2001) Machine Learning , vol.45 , Issue.2 , pp. 187-218
    • Tiňo, P.1    Dorffner, G.2
  • 41
    • 84949726999 scopus 로고    scopus 로고
    • J. Lafferty, C.K.I. Williams, J. Shawe-Taylor, R. S. Zemel, and A. Culotta (Eds.), Advances in neural information processing systems, 23. Red Hook, NY: Curran Associates.
    • Triefenbach, F., Jalalvand, A., Schrauwen, B., and Martens, J. (2010). Phoneme recognition with large hierarchical reservoirs. In J. Lafferty, C.K.I. Williams, J. Shawe-Taylor, R. S. Zemel, and A. Culotta (Eds.), Advances in neural information processing systems, 23 (pp. 2307-2315). Red Hook, NY: Curran Associates.
    • (2010) Phoneme recognition with large hierarchical reservoirs , pp. 2307-2315
    • Triefenbach, F.1    Jalalvand, A.2    Schrauwen, B.3    Martens, J.4
  • 42
    • 79959400804 scopus 로고    scopus 로고
    • Memory versus nonlinearity in reservoirs. In The 2010 International Joint Conference on Neural Networks
    • Piscataway, NJ: IEEE Press.
    • Verstraeten, D., Dambre, J., Dutoit, X., and Schrauwen, B. (2010). Memory versus nonlinearity in reservoirs. In The 2010 International Joint Conference on Neural Networks (pp. 1-8). Piscataway, NJ: IEEE Press.
    • (2010) , pp. 1-8
    • Verstraeten, D.1    Dambre, J.2    Dutoit, X.3    Schrauwen, B.4
  • 43
    • 84887006782 scopus 로고    scopus 로고
    • The unified reservoir computing concept and its digital hardware implementations. In Proc. of the EPFL-LATSIS Symposium 2006
    • Swiss Federal Institute of Technology at Lausanne.
    • Verstraeten, D., Schrauwen, B., D'Haene, M., and Stroobandt, D. (2006). The unified reservoir computing concept and its digital hardware implementations. In Proc. of the EPFL-LATSIS Symposium 2006 (pp. 139-140). Swiss Federal Institute of Technology at Lausanne.
    • (2006) , pp. 139-140
    • Verstraeten, D.1    Schrauwen, B.2    D'Haene, M.3    Stroobandt, D.4
  • 45
    • 0032482432 scopus 로고    scopus 로고
    • Collective dynamics of "small-world" networks
    • Watts,D. J.,andStrogatz, S. H. (1998). Collective dynamics of "small-world" networks. Nature, 393 (6684), 409-10.1998.
    • (1998) Nature , vol.393 , Issue.6684
    • Watts, D.J.1    Strogatz, S.H.2
  • 46
    • 58849145264 scopus 로고    scopus 로고
    • Stable output feedback in reservoir computing using ridge regression. In Proceedings of the 18th International Conference on Artificial Neural Networks
    • New York: Springer-Verlag.
    • Wyffels, F., Schrauwen, B., and Stroobandt, D. (2008). Stable output feedback in reservoir computing using ridge regression. In Proceedings of the 18th International Conference on Artificial Neural Networks (pp. 808-817). New York: Springer-Verlag.
    • (2008) , pp. 808-817
    • Wyffels, F.1    Schrauwen, B.2    Stroobandt, D.3
  • 47
    • 34249819041 scopus 로고    scopus 로고
    • Decoupled echo state networks with lateral inhibition
    • Xue, Y. Yang, L., and Haykin, S. (2007). Decoupled echo state networks with lateral inhibition. Neural Networks, 20, 365-376.
    • (2007) Neural Networks , vol.20 , pp. 365-376
    • Xue, Y.1    Yang, L.2    Haykin, S.3
  • 48
    • 58049171366 scopus 로고    scopus 로고
    • Echo state networks with decoupled reservoir states. In 18th IEEE International Workshop on Machine Learning for Signal Processing
    • Piscataway, NJ: IEEE.
    • Zhang, B., and Wang, Y. (2008). Echo state networks with decoupled reservoir states. In 18th IEEE International Workshop on Machine Learning for Signal Processing. Piscataway, NJ: IEEE.
    • (2008)
    • Zhang, B.1    Wang, Y.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.