Legends: |
Link to a Model |
Reference cited by multiple papers |

## References and models cited by this paper | ## References and models that cite this paper | |

Amari SI (1990) Differential-geometrical methods in statisticsAnderson JA, Silverstein JW, Ritz SA, Jones RS (1977) Distinctive features, categorical perception, and probability learning: some applications of a neural model. Psychol. Rev. 84:413-451Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7:1129-59 [PubMed]Bertschinger N, Natschläger T (2004) Real-time computation at the edge of chaos in recurrent neural networks. Neural Comput 16:1413-36 [Journal] [PubMed]de_Vries B (1991) Temporal processing with neural networks the development of the gamma model Unpublished doctoral dissertation, University of FloridaDelgado A, Kambhampati C, Warwick K (1995) Dynamic recurrent neural network for system identification and control IEEE Pro Control Theory and Appl 142:307-314Erdogmus D, Hild KE, Principe J (2003) Online entropy manipulation: Stochastic information gradient Signal Proc Lett 10:242-245Erdogmus D, Principe JC (2002) Generalized information potential criterion for adaptive system training. IEEE Trans Neural Netw 13:1035-44 [Journal] [PubMed]Feldkamp LA, Prokhorov DV, Eagen C, Yuan F (1998) Enhanced multistream Kalman filter training for recurrent networks Nonlinear modeling: Advanced black-box techniques, Suykens J:Vandewalle J, ed. pp.29Haykin S (1999) Neural Networks: A Comprehensive Foundation (2nd Ed)Haykin S (2002) Adaptive filter theory (4th ed)Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci U S A 81:3088-92 [PubMed]Jaeger H (2001) The echo state approach to analyzing and training recurrent neural networks GMD Report 148Jaeger H (2002) Short term memory in echo state networks Tech Rep 152 German National Research Center for Information TechnologyJaeger H (2002) Tutorial on training recurrent neural networks, covering BPPT, RTRL,EKF and the echo state network approach Tech Rep 159 German National Research Center for Information TechnologyJaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304:78-80 [Journal] [PubMed]Jeffreys H (1946) An invariant form for the prior probability in estimation problems. Proc Royal Soc Lond A 196:453-461Kailath T (1980) Linear systemsKechriotis G, Zervas E, Manolakos ES (1994) Using recurrent neural networks for adaptive communication channel equalization. IEEE Trans Neural Netw 5:267-78 [Journal] [PubMed]Kremer SC (1995) On the computational power of Elman-style recurrent networks. IEEE Trans Neural Netw 6:1000-4 [Journal] [PubMed]Kuznetsov Y (1998) Elements of applied bifurcation theory (2nd ed.)Langton CG (1990) Computation at the edge of chaos: Phase transitions and emergent computation Physica D 42:12-37Maass W, Legenstein RA, Bertschinger N (2005) Methods for estimating the computational power and generalization capability of neural microcircuits Advances in neural information processing systems, Saul LK:Weiss Y:Bottou L, ed. pp.865Maass W, Natschläger T, Markram H (2002) Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput 14:2531-60 [Journal] [PubMed]Mitchell M, Hraber P, Crutchfield J (1993) Revisiting the edge of chaos: Evolving cellular automata to perform computations Complex Systems 7:89-130Packard N (1988) Adaptation towards the edge of chaos Dynamic patterns in complex systems, Kelso JAS:Mandell AJ:Shlesinger MF, ed. pp.293Pouget A, Sejnowski TJ (1997) Spatial transformations in the parietal cortex using basis functions. J Cogn Neurosci 9:222-37 [Journal] [PubMed]Principe J (2001) Dynamic neural networks and optimal signal processing Neural networks for signal processing, Hu Y:Hwang J, ed. pp.6Principe JC, De_vries B, De_oliviera PG (1993) The gamma filter a new class of adaptive IIR filters with restricted feedback IEEE Trans Signal Process 41:649-656Principe JC, Xu D, Fisher J (2000) Information theoretic learning Unsupervised adaptive filtering: Blind source separation, Haykin S, ed. pp.265Prokhorov D (2005) Echo state networks: Appeal and challenges Proc Intl Joint Conf Neural Networks :1463-1466Prokhorov D, Feldkamp L, Tyukin I (2022) Adaptive behavior with fixed weights in recurrent neural networks: An overview Proc Intl Joint Conf Neural Networks :2018-2022Puskorius GV, Feldkamp LA (1994) Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks. IEEE Trans Neural Netw 5:279-97 [Journal] [PubMed]Puskorius GV, Feldkamp LA (1996) Dynamic neural network methods applied to on-vehicle idle speed control Proc IEEE 84:1407-1420Rao Y, Kim S, Sanchez J, Erdogmus D, Principe JC, Carmena J, Lebedev M, Nicolelis M (2005) Learning mappings in brain machine interfaces with echo state networks IEEE Intl Conf Acoustics, Speech, and Signal ProcessRenyi A (1970) Probability theorySanchez JC (2004) From cortical neural spike trains to behavior: Modeling and analysis Unpublished doctoral dissertation, University of FloridaSantiago RA, Lendaris GG (2004) Context discerning multifunction networks: Reformulating fixed weight neural networks Proc Intl Joint Conf Neural Networks :189-194Shah JV, Poon CS (1999) Linear independence of internal representations in multilayer perceptrons. IEEE Trans Neural Netw 10:10-8 [Journal] [PubMed]Siegelmann HT (1993) Foundations of recurrent neural networks Unpublished doctoral dissertation, Rutgers UniversitySinghal S, Wu L (1989) Training multilayer perceptrons with the extended Kalman algorithm Advances in neural information processing systems, Touretzky DS, ed. pp.133Takens F (1981) Detecting strange attractors in turbulence in dynamical systems and turbulence Lecture Notes In Mathematics 898:366-381Thogula R (2003) Information theoretic self-organization of multiple agents Unpublished masters thesis, University of FloridaWerbos P (1992) Neurocontrol and supervised learning: An overview and evaluation Handbook of intelligent control, White D:Sofge D, ed. pp.65Werbos PJ (1990) Backpropagation through time: what it does and how to do it. Proc IEEE 78:1550-1560Wilde DJ (1964) Optimum seeking methodsWilliams RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks Neural Comput 1:270-280 |