Reservoir computing approaches to recurrent neural network training
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[1] Manvendra Singh,et al. Speech Recognition Using Neural Networks , 2011 .
[2] Helmut Hauser,et al. Echo state networks with filter neurons and a delay&sum readout , 2010, Neural Networks.
[3] R. Miikkulainen. Hopfield Network , 2010, Encyclopedia of Machine Learning and Data Mining.
[4] Robert A. Legenstein,et al. Spiking Neurons Can Learn to Solve Information Bottleneck Problems and Extract Independent Components , 2009, Neural Computation.
[5] Luís A. Alexandre,et al. Reservoir computing for static pattern recognition , 2009, ESANN.
[6] Minoru Asada,et al. Studies on reservoir initialization and dynamics shaping in echo state networks , 2009, ESANN.
[7] Jochen J. Steil,et al. Attractor-based computation with reservoirs for online learning of inverse kinematics , 2009, ESANN.
[8] Benjamin Schrauwen,et al. On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing , 2008, NIPS.
[9] Robert A. Legenstein,et al. A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback , 2008, PLoS Comput. Biol..
[10] Marc Schoenauer,et al. Supervised and Evolutionary Learning of Echo State Networks , 2008, PPSN.
[11] Benjamin Schrauwen,et al. Stable Output Feedback in Reservoir Computing Using Ridge Regression , 2008, ICANN.
[12] R.F. Reinhart,et al. Recurrent Neural Associative Learning of Forward and Inverse Kinematics for Movement Generation of the Redundant PA-10 Robot , 2008, 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems (LAB-RS).
[13] Benjamin Schrauwen,et al. Toward optical signal processing using photonic reservoir computing. , 2008, Optics express.
[14] Marc Schoenauer,et al. Unsupervised learning of echo state networks: balancing the double pole , 2008, GECCO '08.
[15] Benjamin Schrauwen,et al. Band-pass Reservoir Computing , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).
[16] Mahdi Jalili,et al. Reservoir optimization in recurrent neural networks using kronecker kernels , 2008, 2008 IEEE International Symposium on Circuits and Systems.
[17] Hendrik Van Brussel,et al. Pruning and Regularisation in Reservoir Computing: a First Insight , 2008, ESANN.
[18] Samy Bengio,et al. Delay learning and polychronization for reservoir computing , 2008, Neurocomputing.
[19] Jochen J. Steil,et al. Improving reservoirs using intrinsic plasticity , 2008, Neurocomputing.
[20] Benjamin Schrauwen,et al. Compact hardware liquid state machines on FPGA for real-time speech recognition , 2008, Neural Networks.
[21] Geoffrey E. Hinton. Reducing the Dimensionality of Data with Neural , 2008 .
[22] Herbert Jaeger,et al. Discovering multiscale dynamical features with hierarchical Echo State Networks , 2008 .
[23] Wolfgang Maass,et al. Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons , 2007, NIPS.
[24] Robert A. Legenstein,et al. Theoretical Analysis of Learning with Reward-Modulated Spike-Timing-Dependent Plasticity , 2007, NIPS.
[25] Marc Schoenauer,et al. Unsupervised Learning of Echo State Networks: A Case Study in Artificial Embryogeny , 2007, Artificial Evolution.
[26] Benjamin Schrauwen,et al. The Introduction of Time-Scales in Reservoir Computing, Applied to Isolated Digits Recognition , 2007, ICANN.
[27] Jiri Pospichal,et al. Improving the Prediction Accuracy of Echo State Neural Networks by Anti-Oja's Learning , 2007, ICANN.
[28] Herbert Jaeger,et al. Echo state network , 2007, Scholarpedia.
[29] Herbert Jaeger,et al. Overview of Reservoir Recipes , 2007 .
[30] Herbert Jaeger,et al. Optimization and applications of echo state networks with leaky- integrator neurons , 2007, Neural Networks.
[31] John G. Harris,et al. Automatic speech recognition using a predictive echo state network classifier , 2007, Neural Networks.
[32] Mustafa C. Ozturk,et al. An associative memory readout for ESNs with applications to dynamical pattern recognition , 2007, Neural Networks.
[33] Simon Haykin,et al. Decoupled echo state networks with lateral inhibition , 2007, Neural Networks.
[34] Tadashi Yamazaki,et al. The cerebellum as a liquid state machine , 2007, Neural Networks.
[35] Robert A. Legenstein,et al. 2007 Special Issue: Edge of chaos and prediction of computational performance for neural circuit models , 2007 .
[36] Gordon Pipa,et al. 2007 Special Issue: Fading memory and time series prediction in recurrent networks with different forms of plasticity , 2007 .
[37] Hendrik Van Brussel,et al. A first attempt of reservoir pruning for classification problems , 2007, ESANN.
[38] José Carlos Príncipe,et al. Special issue on echo state networks and liquid state machines , 2007, Neural Networks.
[39] Jochen Triesch,et al. Synergies Between Intrinsic and Synaptic Plasticity Mechanisms , 2007, Neural Computation.
[40] Jochen J. Steil,et al. Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning , 2007, Neural Networks.
[41] Benjamin Schrauwen,et al. An experimental unification of reservoir computing methods , 2007, Neural Networks.
[42] Ben Jones,et al. Is there a Liquid State Machine in the Bacterium Escherichia Coli? , 2007, 2007 IEEE Symposium on Artificial Life.
[43] Min Han,et al. Support Vector Echo-State Machine for Chaotic Time-Series Prediction , 2007, IEEE Transactions on Neural Networks.
[44] Jürgen Schmidhuber,et al. Training Recurrent Networks by Evolino , 2007, Neural Computation.
[45] Jochen Triesch,et al. Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm , 2022 .
[46] Mantas Lukoševičius. Echo State Networks with Trained Feedbacks , 2007 .
[47] U. Karmarkar,et al. Timing in the Absence of Clocks: Encoding Time in Neural Network States , 2007, Neuron.
[48] Eduardo D. Sontag,et al. Computational Aspects of Feedback in Neural Circuits , 2006, PLoS Comput. Biol..
[49] Wolfgang Maass,et al. Cerebral Cortex Advance Access published February 15, 2006 A Statistical Analysis of Information- Processing Properties of Lamina-Specific , 2022 .
[50] B. Schölkopf,et al. Modeling Human Motion Using Binary Latent Variables , 2007 .
[51] Terrence J. Sejnowski,et al. What Makes a Dynamical System Computationally Powerful , 2007 .
[52] Jason Weston,et al. Scaling Learning Algorithms toward AI , 2007 .
[53] Thomas Hofmann,et al. Temporal dynamics of information content carried by neurons in the primary visual cortex , 2007 .
[54] José Carlos Príncipe,et al. Analysis and Design of Echo State Networks , 2007, Neural Computation.
[55] Benjamin Schrauwen,et al. An overview of reservoir computing: theory, applications and implementations , 2007, ESANN.
[56] Benjamin Schrauwen,et al. Adapting reservoirs to get Gaussian distributions , 2007 .
[57] Yoshua Bengio,et al. Scaling learning algorithms towards AI , 2007 .
[58] Jochen J. Steil,et al. Intrinsic plasticity for reservoir learning algorithms , 2007, ESANN.
[59] Jochen J. Steil. Several ways to solve the MSO problem , 2007, ESANN.
[60] Benjamin Schrauwen,et al. Linking non-binned spike train kernels to several existing spike train metrics , 2006, ESANN.
[61] Wei Wang,et al. The 6th World Congress on Intelligent Control and Automation , 2006 .
[62] Robert A. Legenstein,et al. Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons , 2006, NIPS.
[63] Danko Nikolic,et al. Temporal dynamics of information content carried by neurons in the primary visual cortex , 2006, NIPS.
[64] Geoffrey E. Hinton,et al. Modeling Human Motion Using Binary Latent Variables , 2006, NIPS.
[65] Peter Ford Dominey,et al. A Neurolinguistic Model of Grammatical Construction Processing , 2006, Journal of Cognitive Neuroscience.
[66] Dan Ventura,et al. Preparing More Effective Liquid State Machines Using Hebbian Learning , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[67] Cheng-jian Wei,et al. Harnessing Non-linearity by Sigmoid-wavelet Hybrid Echo State Networks (SWHESN) , 2006, 2006 6th World Congress on Intelligent Control and Automation.
[68] András Lörincz,et al. Critical Echo State Networks , 2006, ICANN.
[69] Jiri Pospichal,et al. Merging Echo State and Feedforward Neural Networks for Time Series Forecasting , 2006, ICANN.
[70] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[71] Benjamin Schrauwen,et al. Reservoir-based techniques for speech recognition , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.
[72] Mantas Lukoševičius,et al. Time Warping Invariant Echo State Networks , 2006 .
[73] Peter Michael Young,et al. A tighter bound for the echo state property , 2006, IEEE Transactions on Neural Networks.
[74] Jürgen Schmidhuber,et al. Evolino for recurrent support vector machines , 2005, ESANN.
[75] Charles W. Anderson,et al. Exploiting Iso-error Pathways in the N, k-plane to Improve Echo State Network Performance , 2006 .
[76] Carlos Lourenço. Dynamical reservoir properties as network effects , 2006, ESANN.
[77] M. Cernansky,et al. Feed-forward echo state networks , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[78] H. Jaeger,et al. Reservoir riddles: suggestions for echo state network research , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[79] C. Anderson,et al. Modeling reward functions for incomplete state representations via echo state networks , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[80] M. C. Ozturk,et al. Computing with transiently stable states , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[81] Dongming Xu,et al. Direct adaptive control: an echo state network and genetic algorithm approach , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[82] Eduardo D. Sontag,et al. Principles of real-time computing with feedback applied to cortical microcircuit models , 2005, NIPS.
[83] Peter Ford Dominey. From Sensorimotor Sequence to Grammatical Construction: Evidence from Simulation and Neurophysiology , 2005, Adapt. Behav..
[84] B. Schrauwen,et al. Isolated word recognition with the Liquid State Machine: a case study , 2005, Inf. Process. Lett..
[85] Jochen J. Steil,et al. Memory in Backpropagation-Decorrelation O(N) Efficient Online Recurrent Learning , 2005, ICANN.
[86] Jochen Triesch,et al. A Gradient Rule for the Plasticity of a Neuron's Intrinsic Excitability , 2005, ICANN.
[87] Julian Eggert,et al. Short Term Memory and Pattern Matching with Simple Echo State Networks , 2005, ICANN.
[88] P. Levi,et al. Meta-Learning for Adaptive Identification of Non-Linear Dynamical Systems , 2005, Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005..
[89] W. Gerstner,et al. Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[90] Jochen J. Steil,et al. Analyzing the weight dynamics of recurrent learning algorithms , 2005, Neurocomputing.
[91] Daniel Richardson,et al. Linear Algebra for Time Series of Spikes , 2005, ESANN.
[92] Benjamin Schrauwen,et al. Reservoir Computing with Stochastic Bitstream Neurons , 2005 .
[93] Jochen J. Steil. Stability of backpropagation-decorrelation efficient O(N) recurrent learning , 2005, ESANN.
[94] Johannes Schemmel,et al. Edge of Chaos Computation in Mixed-Mode VLSI - A Hard Liquid , 2004, NIPS.
[95] Jochen Triesch,et al. Synergies between Intrinsic and Synaptic Plasticity in Individual Model Neurons , 2004, NIPS.
[96] Robert A. Legenstein,et al. Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits , 2004, NIPS.
[97] T. van der Zant,et al. Identification of motion with echo state network , 2004, Oceans '04 MTS/IEEE Techno-Ocean '04 (IEEE Cat. No.04CH37600).
[98] J.J. Steil,et al. Backpropagation-decorrelation: online recurrent learning with O(N) complexity , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[99] Nils Bertschinger,et al. Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks , 2004, Neural Computation.
[100] Harald Haas,et al. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.
[101] G. Miller. Learning to Forget , 2004, Science.
[102] Norbert Michael Mayer,et al. Echo State Networks and Self-Prediction , 2004, BioADIT.
[103] Marcus Kaiser,et al. Spatial growth of real-world networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[104] Peter Ford Dominey. Complex sensory-motor sequence learning based on recurrent state representation and reinforcement learning , 1995, Biological Cybernetics.
[105] Henry Markram,et al. Computational models for generic cortical microcircuits , 2004 .
[106] Shinji Kusumoto,et al. Biologically Inspired Approaches to Advanced Information Technology , 2004, Lecture Notes in Computer Science.
[107] Benjamin Liebald,et al. Exploration of effects of different network topologies on the ESN signal crosscorrelation matrix spectrum , 2004 .
[108] Peter Ford Dominey,et al. Identification of prosodic attitudes by a temporal recurrent network. , 2003, Brain research. Cognitive brain research.
[109] Chrisantha Fernando,et al. Pattern Recognition in a Bucket , 2003, ECAL.
[110] Peter Ford Dominey,et al. Neurological basis of language and sequential cognition: Evidence from simulation, aphasia, and ERP studies , 2003, Brain and Language.
[111] Robert M. French,et al. Catastrophic interference in connectionist networks , 2003 .
[112] Henry Markram,et al. Computer models and analysis tools for neural microcircuits , 2003 .
[113] Henry Markram,et al. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.
[114] Chris I. De Zeeuw,et al. Dynamical Working Memory and Timed Responses: The Role of Reverberating Loops in the Olivo-Cerebellar System , 2002, Neural Computation.
[115] Danil V. Prokhorov,et al. Adaptive behavior with fixed weights in RNN: an overview , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).
[116] D. Munday. Edge of chaos. , 2002, Journal of the Royal Society of Medicine.
[117] Herbert Jaeger,et al. Adaptive Nonlinear System Identification with Echo State Networks , 2002, NIPS.
[118] Henry Markram,et al. A Model for Real-Time Computation in Generic Neural Microcircuits , 2002, NIPS.
[119] D. Wolpert. The Supervised Learning No-Free-Lunch Theorems , 2002 .
[120] Herbert Jaeger,et al. The''echo state''approach to analysing and training recurrent neural networks , 2001 .
[121] Jürgen Schmidhuber,et al. Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.
[122] Amir F. Atiya,et al. New results on recurrent network training: unifying the algorithms and accelerating convergence , 2000, IEEE Trans. Neural Networks Learn. Syst..
[123] Naftali Tishby,et al. The information bottleneck method , 2000, ArXiv.
[124] Peter Ford Dominey,et al. Neural network processing of natural language: I. Sensitivity to serial, temporal and abstract structure of language in the infant , 2000 .
[125] Albert,et al. Emergence of scaling in random networks , 1999, Science.
[126] G B Stanley,et al. Reconstruction of Natural Scenes from Ensemble Responses in the Lateral Geniculate Nucleus , 1999, The Journal of Neuroscience.
[127] Tafsir Thiam,et al. The Boltzmann machine , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).
[128] Christof Koch,et al. How voltage-dependent conductances can adapt to maximize the information encoded by neuronal firing rate , 1999, Nature Neuroscience.
[129] B. Farhang-Boroujeny,et al. Adaptive Filters: Theory and Applications , 1999 .
[130] Emile H. L. Aarts,et al. Boltzmann machines , 1998 .
[131] Duncan J. Watts,et al. Collective dynamics of ‘small-world’ networks , 1998, Nature.
[132] H. Markram,et al. Differential signaling via the same axon of neocortical pyramidal neurons. , 1998, Proceedings of the National Academy of Sciences of the United States of America.
[133] Chuanyi Ji,et al. Fast training of recurrent networks based on the EM algorithm , 1998, IEEE Trans. Neural Networks.
[134] L. Abbott,et al. Responses of neurons in primary and inferior temporal visual cortices to natural scenes , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.
[135] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[136] Yoshua Bengio,et al. Hierarchical Recurrent Neural Networks for Long-Term Dependencies , 1995, NIPS.
[137] Terrence J. Sejnowski,et al. An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.
[138] M M Merzenich,et al. Temporal information transformed into a spatial code by a neural network with realistic properties , 1995, Science.
[139] Lee A. Feldkamp,et al. Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks , 1994, IEEE Trans. Neural Networks.
[140] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[141] James P. Crutchfield,et al. Dynamics, computation, and the “edge of chaos”: a re-examination , 1993, adap-org/9306003.
[142] Yuichi Nakamura,et al. Approximation of dynamical systems by continuous time recurrent neural networks , 1993, Neural Networks.
[143] K. Doya,et al. Bifurcations in the learning of recurrent neural networks , 1992, [Proceedings] 1992 IEEE International Symposium on Circuits and Systems.
[144] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[145] Paul J. Werbos,et al. Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.
[146] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.
[147] M. V. Rossum,et al. In Neural Computation , 2022 .
[148] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[149] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[150] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.
[151] F. Takens. Detecting strange attractors in turbulence , 1981 .
[152] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[153] W. Kautz. Transient synthesis in the time domain , 1954 .
[154] J. Knott. The organization of behavior: A neuropsychological theory , 1951 .
[155] D. Hebb. The Organization of behavior : a neuropsychological theory / D.O. Hebb , 1949 .
[156] W. Maass,et al. What makes a dynamical system computationally powerful ? , 2022 .