On the public citation graph
A learning algorithm to teach spatiotemporal patterns to recurrent neural networks
0 signed · 77 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/bf00198098openalex-snapshot
A real time learning algorithm for recurrent analog neural networks
inferredcites →https://doi.org/10.1038/333452a0openalex-snapshot
Network model of shape-from-shading: neural function arises from both receptive and projective fields
inferredcites →https://doi.org/10.1073/pnas.79.8.2554openalex-snapshot
Neural networks and physical systems with emergent collective computational abilities.
inferredcites →https://doi.org/10.1073/pnas.81.10.3088openalex-snapshot
Neurons with graded response have collective computational properties like those of two-state neurons.
inferredcites →https://doi.org/10.1103/physrevlett.59.2229openalex-snapshot
Generalization of back-propagation to recurrent neural networks
inferredcites →https://doi.org/10.1109/ijcnn.1989.118724openalex-snapshot
Learning state space trajectories in recurrent neural networks
inferredcites →https://doi.org/10.1109/tsmc.1983.6313075openalex-snapshot
Absolute stability of global pattern formation and parallel memory storage by competitive neural networks
inferredcites →https://doi.org/10.1162/neco.1989.1.2.263openalex-snapshot
Learning State Space Trajectories in Recurrent Neural Networks
inferredcites →https://doi.org/10.1162/neco.1989.1.2.270openalex-snapshot
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
inferredcites →https://doi.org/10.21236/ada164453openalex-snapshot
Learning Internal Representations by Error Propagation
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/(sici)1520-684x(200004)31:4<77::aid-scj8>3.0.co;2-yopenalex-snapshot
Dynamical systems produced by recurrent neural networks
inferred← citeshttps://doi.org/10.1002/1520-6440(200106)84:6<23::aid-ecjc3>3.0.co;2-7openalex-snapshot
Learning chaotic dynamics under noise with on-line EM algorithm
inferred← citeshttps://doi.org/10.1002/1520-684x(200103)32:3<14::aid-scj2>3.0.co;2-uopenalex-snapshot
Fluctuation-driven learning rule for continuous-time recurrent neural networks and its application to dynamical system control
inferred← citeshttps://doi.org/10.1002/aic.690391009openalex-snapshot
Dynamic process modeling with recurrent neural networks
inferred← citeshttps://doi.org/10.1002/ecjc.4430790807openalex-snapshot
Memorizing and regenerating spatiotemporal patterns with a structured recurrent neural network
inferred← citeshttps://doi.org/10.1002/eej.10011openalex-snapshot
Neural network approximation to nonlinear dynamics by velocity error backpropagation
inferred← citeshttps://doi.org/10.1002/scj.1206openalex-snapshot
Recurrent neural network with short‐term memory and fast structural learning method
inferred← citeshttps://doi.org/10.1002/scj.4690221008openalex-snapshot
A realtime learning algorithm for recurrent neural networks
inferred← citeshttps://doi.org/10.1002/scj.4690231308openalex-snapshot
Learning algorithms and the shape of the learning surface in recurrent neural networks
inferred← citeshttps://doi.org/10.1002/scj.4690271203openalex-snapshot
Comparative study of searching method in recurrent neural networks
inferred← citeshttps://doi.org/10.1007/978-1-4613-0269-8_43openalex-snapshot
Mathematical Models Used for Sound Synthesis
inferred← citeshttps://doi.org/10.1007/978-3-642-04277-5_60openalex-snapshot
Approximation Capability of Continuous Time Recurrent Neural Networks for Non-autonomous Dynamical Systems
inferred← citeshttps://doi.org/10.1007/bf00202792openalex-snapshot
Embedding a grammatical description in deterministic chaos: an experiment in recurrent neural learning
inferred← citeshttps://doi.org/10.1016/0020-0255(94)90097-3openalex-snapshot
An adaptive fuzzy system for modeling chaos
inferred← citeshttps://doi.org/10.1016/0166-1280(92)87132-jopenalex-snapshot
Application of neural networks with feedback connections in chemistry: prediction of 13C NMR chemical shifts in a series of monosubstituted benzenes
inferred← citeshttps://doi.org/10.1016/0893-6080(94)90017-5openalex-snapshot
Oscillatory neural network and learning of continuously transformed patterns
inferred← citeshttps://doi.org/10.1016/0893-6080(94)90098-1openalex-snapshot
Classification of temporal trajectories by continuous-time recurrent nets
inferred← citeshttps://doi.org/10.1016/0925-2312(95)00093-3openalex-snapshot
Chaotic recurrent neural networks and their application to speech recognition
inferred← citeshttps://doi.org/10.1016/b978-0-444-89488-5.50066-xopenalex-snapshot
Training the Gradient Field of a Dynamic Hopfield (Recurrent) Network for Classification
inferred← citeshttps://doi.org/10.1016/s0893-6080(01)00094-6openalex-snapshot
Reconstruction of chaotic dynamics by on-line EM algorithm
inferred← citeshttps://doi.org/10.1016/s0893-6080(03)00076-5openalex-snapshot
Back-propagation learning of infinite-dimensional dynamical systems
inferred← citeshttps://doi.org/10.1016/s0893-6080(05)80008-5openalex-snapshot
The construction of arbitrary stable dynamics in nonlinear neural networks
inferred← citeshttps://doi.org/10.1016/s0893-6080(05)80125-xopenalex-snapshot
Approximation of dynamical systems by continuous time recurrent neural networks
inferred← citeshttps://doi.org/10.1016/s0893-6080(98)00098-7openalex-snapshot
Learning dynamical systems by recurrent neural networks from orbits
inferred← citeshttps://doi.org/10.1049/el:19961009openalex-snapshot
Interpolation of LSP coefficients using recurrentneural networks
inferred← citeshttps://doi.org/10.1080/00207729508929062openalex-snapshot
Stochastic sensitivity analysis method for neural network learning
inferred← citeshttps://doi.org/10.1109/5.58324openalex-snapshot
Mathematical foundations of neurocomputing
inferred← citeshttps://doi.org/10.1109/72.329692openalex-snapshot
Hopfield net generation, encoding and classification of temporal trajectories
inferred← citeshttps://doi.org/10.1109/81.828569openalex-snapshot
Nonlinear system identification using additive dynamic neural networks-two on-line approaches
inferred← citeshttps://doi.org/10.1109/apccas.1994.514515openalex-snapshot
A module structured recurrent neural network capable of memorizing and regenerating dynamics
inferred← citeshttps://doi.org/10.1109/cdc.1993.325709openalex-snapshot
Dynamic memory by recurrent neural network and its learning by genetic algorithm
inferred← citeshttps://doi.org/10.1109/etfa.1993.396427openalex-snapshot
Heuristic learning by genetic algorithm for recurrent neural network
inferred← citeshttps://doi.org/10.1109/icnn.1994.374315openalex-snapshot
Robustness of recurrent neural networks against deformation of external input patterns
inferred← citeshttps://doi.org/10.1109/icnn.1994.374325openalex-snapshot
Smooth recollection of a pattern sequence by nonmonotone analog neural networks
inferred← citeshttps://doi.org/10.1109/icnn.1994.374732openalex-snapshot
The inverse method for recurrent neural networks
inferred← citeshttps://doi.org/10.1109/icnn.1995.487258openalex-snapshot
Learning Dynamical Systems from Trajectories by Continuous Time Recurrent Neural Networks
inferred← citeshttps://doi.org/10.1109/icnn.1995.487719openalex-snapshot
Eye movement model with neural oscillators
inferred← citeshttps://doi.org/10.1109/icnn.1995.488245openalex-snapshot
Learning chaotic dynamics in recurrent RBF network
inferred← citeshttps://doi.org/10.1109/icslp.1996.607116openalex-snapshot
An application of recurrent neural networks to low bit rate speech coding
inferred← citeshttps://doi.org/10.1109/icsmc.1996.571143openalex-snapshot
An analysis of the chaotic transition of model muscle tremor mechanism obtained by artificial neural network
inferred← citeshttps://doi.org/10.1109/icsmc.1999.814127openalex-snapshot
Reconstruction of chaotic dynamics and robustness to noise with on-line EM algorithm
inferred← citeshttps://doi.org/10.1109/icsmc.2000.886013openalex-snapshot
Automated trend diagnosis using neural networks
inferred← citeshttps://doi.org/10.1109/ijcnn.1990.137631openalex-snapshot
APOLONN brings us to the real world: learning nonlinear dynamics and fluctuations in nature
inferred← citeshttps://doi.org/10.1109/ijcnn.1991.155284openalex-snapshot
Fast temporal neural learning using teacher forcing
inferred← citeshttps://doi.org/10.1109/ijcnn.1991.170489openalex-snapshot
Learning process of recurrent neural networks
inferred← citeshttps://doi.org/10.1109/ijcnn.1992.227073openalex-snapshot
Valley searching method for recurrent neural networks
inferred← citeshttps://doi.org/10.1109/ijcnn.1992.227279openalex-snapshot
Learning of continuously transformed pattern cycles by an oscillatory neural network
inferred← citeshttps://doi.org/10.1109/ijcnn.1993.714167openalex-snapshot
Speech recognition by dynamic recurrent neural networks
inferred← citeshttps://doi.org/10.1109/ijcnn.1993.714178openalex-snapshot
Back-propagation learning of an infinite-dimensional dynamical system
inferred← citeshttps://doi.org/10.1109/ijcnn.1993.714189openalex-snapshot
Time sequential pattern transformation and attractors of recurrent neural networks
inferred← citeshttps://doi.org/10.1109/ijcnn.1993.714250openalex-snapshot
A measure theoretical analysis of learning algorithms for recurrent neural networks
inferred← citeshttps://doi.org/10.1109/ijcnn.1999.833444openalex-snapshot
Design method of neural networks for limit cycle generator
inferred← citeshttps://doi.org/10.1109/ijcnn.2000.861358openalex-snapshot
Design method of limit cycle generator by hysteresis neural networks
inferred← citeshttps://doi.org/10.1109/iros.1993.583874openalex-snapshot
Learning scheme for recurrent neural network by genetic algorithm
inferred← citeshttps://doi.org/10.1109/isic.1992.225084openalex-snapshot
State-space approach to continuous recurrent neural networks
inferred← citeshttps://doi.org/10.1109/nnsp.1998.710666openalex-snapshot
On-line EM algorithm and reconstruction of chaotic dynamics
inferred← citeshttps://doi.org/10.1109/nnsp.2002.1030046openalex-snapshot
A fingerprint segmentation method using a recurrent neural network
inferred← citeshttps://doi.org/10.1142/s0218127400001055openalex-snapshot
POINCARÉ MAPPING OF CONTINUOUS RECURRENT NEURAL NETWORKS EXCITED BY TEMPORAL EXTERNAL INPUT
inferred← citeshttps://doi.org/10.1142/s0218127401002158openalex-snapshot
FRACTAL TRANSITION IN CONTINUOUS RECURRENT NEURAL NETWORKS
inferred← citeshttps://doi.org/10.11509/isciesci.43.10_519openalex-snapshot
ニューラルネットワークによる学習の収束性と非線形ダイナミクス (高機能化を目指した非線形システム制御総合特集号-I)
inferred← citeshttps://doi.org/10.1162/neco.1993.5.5.795openalex-snapshot
Recurrent and Feedforward Polynomial Modeling of Coupled Time Series
inferred← citeshttps://doi.org/10.1541/ieejeiss1987.118.7-8_1148openalex-snapshot
A Plant Status Prediction Method by Neural Network Using Simplified Process Data Components Based on Frequencies
inferred← citeshttps://doi.org/10.1541/ieejeiss1987.121.6_1026openalex-snapshot
Neural Network Approximation to Nonlinear Dynamics by Velocity Error Backpropagation
inferred← citeshttps://doi.org/10.25911/5d7a294802e6fopenalex-snapshot
Representing Time in Automated Speech Recognition
inferred← citeshttps://doi.org/10.5821/dissertation-2117-93270openalex-snapshot
Contribución a la identificación de sistemas dinámicos mediate métodos conexionistas
inferred← citeshttps://doi.org/10.9746/sicetr1965.32.957openalex-snapshot
Creating Attractors Using BPTT Algorithm with Recurrent Neural Networks
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.