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Advances in neural information processing systems 1
Implications of recursive distributed representations
Advances in neural information processing systems 1
Learning sequential structure in simple recurrent networks
Advances in neural information processing systems 1
Recursive distributed representations
Artificial Intelligence - On connectionist symbol processing
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
NETtalk: a parallel network that learns to read aloud
Neurocomputing: foundations of research
Higher order recurrent networks and grammatical inference
Advances in neural information processing systems 2
Representation and structure in connectionist models
Cognitive models of speech processing
A theory for neural networks with time delays
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Neural networks for spatial-temporal pattern recognition
Handbook of neural computer applications
Graded State Machines: The Representation of Temporal Contingencies in Simple Recurrent Networks
Machine Learning - Connectionist approaches to language learning
Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
Local feedback multilayered networks
Neural Computation
Induction of finite-state languages using second-order recurrent networks
Neural Computation
The neural network pushdown automation: model, stack and learning simulations
The neural network pushdown automation: model, stack and learning simulations
Learning long-term dependencies is not as difficult with NARX recurrent neural networks
Learning long-term dependencies is not as difficult with NARX recurrent neural networks
Constructing deterministic finite-state automata in recurrent neural networks
Journal of the ACM (JACM)
On the effect of analog noise in discrete-time analog computations
Neural Computation
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Switching and Finite Automata Theory: Computer Science Series
Switching and Finite Automata Theory: Computer Science Series
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
Unified Integration of Explicit Knowledge and Learning by Example in Recurrent Networks
IEEE Transactions on Knowledge and Data Engineering
Training Second-Order Recurrent Neural Networks using Hints
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Using Prior Knowledge in a {NNPDA} to Learn Context-Free Languages
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Neural Methodology for Prediction and Identification of Non-linear Dynamic Systems
NICROSP '96 Proceedings of the 1996 International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing (NICROSP '96)
Neural Computation
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy finite-state automata can be deterministically encoded into recurrent neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
Learning long-term dependencies in NARX recurrent neural networks
IEEE Transactions on Neural Networks
Computational capabilities of local-feedback recurrent networks acting as finite-state machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
On the computational power of Elman-style recurrent networks
IEEE Transactions on Neural Networks
Gradient calculations for dynamic recurrent neural networks: a survey
IEEE Transactions on Neural Networks
Architectural bias in recurrent neural networks: fractal analysis
Neural Computation
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Rule Extraction from Recurrent Neural Networks: A Taxonomy and Review
Neural Computation
Universal Approximation Capability of Cascade Correlation for Structures
Neural Computation
The Crystallizing Substochastic Sequential Machine Extractor: CrySSMEx
Neural Computation
Spatio-temporal memories for machine learning: a long-term memory organization
IEEE Transactions on Neural Networks
Unsupervised recursive sequence processing
Neurocomputing
Closed loop stability of FIR-recurrent neural networks
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Adaptive self-scaling non-monotone BFGS training algorithm for recurrent neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Functional architectures and hierarchies of time scales
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
How do we measure and improve the quality of a hierarchical ontology?
Journal of Systems and Software
A hybrid neural model in long-term electrical load forecasting
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Hourly forecasting of SO2 pollutant concentration using an elman neural network
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Information Sciences: an International Journal
Robotics and Autonomous Systems
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This article reviews connectionist network architectures and training algorithms that are capable of dealing with patterns distributed across both space and time - spatiotemporal patterns. It provides common mathematical, algorithmic, and illustrative frameworks for describing spatiotemporal networks, making it easier to compare and contrast their representational and operational characteristics. Computational power, representational issues, and learning are discussed. In additional references to the relevant source publications are provided. This article can serve as a guide to prospective users of spatiotemporal networks by providing an overview of the operational and representational alternatives available.